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Web Mining - Tutorial
 
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Web Mining Web Mining is the use of Data mining techniques to automatically discover and extract information from World Wide Web. There are 3 areas of web Mining Web content Mining. Web usage Mining Web structure Mining. Web content Mining Web content Mining is the process of extracting useful information from content of web document.it may consists of text images,audio,video or structured record such as list & tables. screen scaper,Mozenda,Automation Anywhere,Web content Extractor, Web info extractor are the tools used to extract essential information that one needs. Web Usage Mining Web usage Mining is the process of identifying browsing patterns by analysing the users Navigational behaviour. Techniques for discovery & pattern analysis are two types. They are Pattern Analysis Tool. Pattern Discovery Tool. Data pre processing,Path Analysis,Grouping,filtering,Statistical Analysis, Association Rules,Clustering,Sequential Pattterns,classification are the Analysis done to analyse the patterns. Web structure Mining Web structure Mining is a tool, used to extract patterns from hyperlinks in the web. Web structure Mining is also called link Mining. HITS & PAGE RANK Algorithm are the Popular Web structure Mining Algorithm. By applying Web content mining,web structure Mining & Web usage Mining knowledge is extracted from web data.
What is STRUCTURE MINING? What does STRUCTURE MINING mean? STRUCTURE MINING meaning & explanation
 
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What is STRUCTURE MINING? What does STRUCTURE MINING mean? STRUCTURE MINING meaning - STRUCTURE MINING definition - STRUCTURE MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Structure mining or structured data mining is the process of finding and extracting useful information from semi-structured data sets. Graph mining, sequential pattern mining and molecule mining are special cases of structured data mining. The growth of the use of semi-structured data has created new opportunities for data mining, which has traditionally been concerned with tabular data sets, reflecting the strong association between data mining and relational databases. Much of the world's interesting and mineable data does not easily fold into relational databases, though a generation of software engineers have been trained to believe this was the only way to handle data, and data mining algorithms have generally been developed only to cope with tabular data. XML, being the most frequent way of representing semi-structured data, is able to represent both tabular data and arbitrary trees. Any particular representation of data to be exchanged between two applications in XML is normally described by a schema often written in XSD. Practical examples of such schemata, for instance NewsML, are normally very sophisticated, containing multiple optional subtrees, used for representing special case data. Frequently around 90% of a schema is concerned with the definition of these optional data items and sub-trees. Messages and data, therefore, that are transmitted or encoded using XML and that conform to the same schema are liable to contain very different data depending on what is being transmitted. Such data presents large problems for conventional data mining. Two messages that conform to the same schema may have little data in common. Building a training set from such data means that if one were to try to format it as tabular data for conventional data mining, large sections of the tables would or could be empty. There is a tacit assumption made in the design of most data mining algorithms that the data presented will be complete. The other necessity is that the actual mining algorithms employed, whether supervised or unsupervised, must be able to handle sparse data. Namely, machine learning algorithms perform badly with incomplete data sets where only part of the information is supplied. For instance methods based on neural networks. or Ross Quinlan's ID3 algorithm. are highly accurate with good and representative samples of the problem, but perform badly with biased data. Most of times better model presentation with more careful and unbiased representation of input and output is enough. A particularly relevant area where finding the appropriate structure and model is the key issue is text mining. XPath is the standard mechanism used to refer to nodes and data items within XML. It has similarities to standard techniques for navigating directory hierarchies used in operating systems user interfaces. To data and structure mine XML data of any form, at least two extensions are required to conventional data mining. These are the ability to associate an XPath statement with any data pattern and sub statements with each data node in the data pattern, and the ability to mine the presence and count of any node or set of nodes within the document. As an example, if one were to represent a family tree in XML, using these extensions one could create a data set containing all the individuals in the tree, data items such as name and age at death, and counts of related nodes, such as number of children. More sophisticated searches could extract data such as grandparents' lifespans etc. The addition of these data types related to the structure of a document or message facilitates structure mining.
Views: 369 The Audiopedia
Extract Structured Data from unstructured Text (Text Mining Using R)
 
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A very basic example: convert unstructured data from text files to structured analyzable format.
Views: 11404 Stat Pharm
text mining, web mining and sentiment analysis
 
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text mining, web mining
Views: 1520 Kakoli Bandyopadhyay
Website Basic Structure and Navigation -  Web Design Basics - Episode 2
 
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Understanding Website Basic Structure and Website Navigation - Hierarchical Structure of Website including Web Navigation Elements and Website Menus.
Views: 10666 Weboq
BigDataX: Structure of the web
 
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Big Data Fundamentals is part of the Big Data MicroMasters program offered by The University of Adelaide and edX. Learn how big data is driving organisational change and essential analytical tools and techniques including data mining and PageRank algorithms. Enrol now! http://bit.ly/2rg1TuF
"Text Mining Unstructured Corporate Filing Data" by Yin Luo
 
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Yin Luo, Vice Chairman at Wolfe Research, LLC presented this talk at QuantCon NYC 2017. In this talk, he showcases how web scraping, distributed cloud computing, NLP, and machine learning techniques can be applied to systematically analyze corporate filings from the EDGAR database. Equipped with his own NLP algorithms, he studies a wide range of models based on corporate filing data: measuring the document tone or sentiment with finance oriented lexicons; investigating the changes in the language structure; computing the proportion of numeric versus textual information, and estimating the word complexity in corporate filings; and lastly, using machine learning algorithms to quantify the informative contents. His NLP-based stock selection signals have strong and consistent performance, with low turnover and slow decay, and is uncorrelated to traditional factors. ------- Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Views: 1699 Quantopian
Identifying Important Features of Users to Improve Page Ranking Algorithms
 
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Web is a wide, various and dynamic environment in which different users publish their documents. Web-mining is one of data mining applications in which web patterns are explored. Studies on web mining can be categorized into three classes: application mining, content mining and structure mining. Today, internet has found an increasing significance. Search engines are considered as an important tool to respond users’ interactions. Among algorithms which is used to find pages desired by users is page rank algorithm which ranks pages based on users’ interests. However, as being the most widely used algorithm by search engines including Google, this algorithm has proved its eligibility compared to similar algorithm, but considering growth speed of Internet and increase in using this technology, improving performance of this algorithm is considered as one of the web mining necessities. Current study emphasizes on Ant Colony algorithm and marks most visited links based on higher amount of pheromone. Results of the proposed algorithm indicate high accuracy of this method compared to previous methods. Ant Colony Algorithm as one of the swarm intelligence algorithms inspired by social behavior of ants can be effective in modeling social behavior of web users. In addition, application mining and structure mining techniques can be used simultaneously to improve page ranking performance.
Views: 6 IJWEST JOURNAL
What is TEXT MINING? What does TEXT MINING mean? TEXT MINING meaning, definition & explanation
 
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What is TEXT MINING? What does TEXT MINING mean? TEXT MINING meaning - TEXT MINING definition - TEXT MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities). Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The overarching goal is, essentially, to turn text into data for analysis, via application of natural language processing (NLP) and analytical methods. A typical application is to scan a set of documents written in a natural language and either model the document set for predictive classification purposes or populate a database or search index with the information extracted. The term text analytics describes a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources for business intelligence, exploratory data analysis, research, or investigation. The term is roughly synonymous with text mining; indeed, Ronen Feldman modified a 2000 description of "text mining" in 2004 to describe "text analytics." The latter term is now used more frequently in business settings while "text mining" is used in some of the earliest application areas, dating to the 1980s, notably life-sciences research and government intelligence. The term text analytics also describes that application of text analytics to respond to business problems, whether independently or in conjunction with query and analysis of fielded, numerical data. It is a truism that 80 percent of business-relevant information originates in unstructured form, primarily text. These techniques and processes discover and present knowledge – facts, business rules, and relationships – that is otherwise locked in textual form, impenetrable to automated processing.
Views: 2090 The Audiopedia
TEXT CLASSIFICATION ALGORITHM IN DATA MINNING
 
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A lot of side-information is available along with the text documents in online forums. Information may be of different kinds, such as the links in the document, user-access behavior from web logs, or other non-textual attributes which are embedded into the text document. The relative importance of this side-information may be difficult to estimate, especially when some of the information is noisy., or can add noise to the process. It can be risky to incorporate side information into the clustering process, because it can either improve the quality of the representation for clustering
Views: 186 Dhivya Balu
On The Use Of Side Information For Mining Text Data
 
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In many text mining applications, side-information is available along with the text documents. Such side-information may be of different kinds, such as document provenance information, the links in the document, user-access behavior from web logs, or other non-textual attributes which are embedded into the text document. Such attributes may contain a tremendous amount of information for clustering purposes.
Views: 216 Gtek
Facilitating Effective User Navigation through Website Structure Improvement
 
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Facilitating Effective User Navigation through Website Structure Improvement ieee data mining 2013 project Read more at: http://ieee-projects10.com/facilitating-effective-user-navigation-through-website-structure-improvement/
Views: 507 satya narayana
Web Data Mining
 
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Data mining tools for getting similarity and classification among different websites.(Naive Bayes Classifier, k-means,others)
Views: 121 Juan Carlos Ucles
Data Mining-Structured Data, Unstructured data and Information Retrieval
 
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Structured Data, Unstructured data and Information Retrieval
Views: 1326 John Paul
Decision Tree with Solved Example in English | DWM | ML | BDA
 
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Take the Full Course of Artificial Intelligence What we Provide 1) 28 Videos (Index is given down) 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in Artificial Intelligence Sample Notes : https://goo.gl/aZtqjh To buy the course click https://goo.gl/H5QdDU if you have any query related to buying the course feel free to email us : [email protected] Other free Courses Available : Python : https://goo.gl/2gftZ3 SQL : https://goo.gl/VXR5GX Arduino : https://goo.gl/fG5eqk Raspberry pie : https://goo.gl/1XMPxt Artificial Intelligence Index 1)Agent and Peas Description 2)Types of agent 3)Learning Agent 4)Breadth first search 5)Depth first search 6)Iterative depth first search 7)Hill climbing 8)Min max 9)Alpha beta pruning 10)A* sums 11)Genetic Algorithm 12)Genetic Algorithm MAXONE Example 13)Propsotional Logic 14)PL to CNF basics 15) First order logic solved Example 16)Resolution tree sum part 1 17)Resolution tree Sum part 2 18)Decision tree( ID3) 19)Expert system 20) WUMPUS World 21)Natural Language Processing 22) Bayesian belief Network toothache and Cavity sum 23) Supervised and Unsupervised Learning 24) Hill Climbing Algorithm 26) Heuristic Function (Block world + 8 puzzle ) 27) Partial Order Planing 28) GBFS Solved Example
Views: 197006 Last moment tuitions
R tutorial: What is text mining?
 
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Learn more about text mining: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Hi, I'm Ted. I'm the instructor for this intro text mining course. Let's kick things off by defining text mining and quickly covering two text mining approaches. Academic text mining definitions are long, but I prefer a more practical approach. So text mining is simply the process of distilling actionable insights from text. Here we have a satellite image of San Diego overlaid with social media pictures and traffic information for the roads. It is simply too much information to help you navigate around town. This is like a bunch of text that you couldn’t possibly read and organize quickly, like a million tweets or the entire works of Shakespeare. You’re drinking from a firehose! So in this example if you need directions to get around San Diego, you need to reduce the information in the map. Text mining works in the same way. You can text mine a bunch of tweets or of all of Shakespeare to reduce the information just like this map. Reducing the information helps you navigate and draw out the important features. This is a text mining workflow. After defining your problem statement you transition from an unorganized state to an organized state, finally reaching an insight. In chapter 4, you'll use this in a case study comparing google and amazon. The text mining workflow can be broken up into 6 distinct components. Each step is important and helps to ensure you have a smooth transition from an unorganized state to an organized state. This helps you stay organized and increases your chances of a meaningful output. The first step involves problem definition. This lays the foundation for your text mining project. Next is defining the text you will use as your data. As with any analytical project it is important to understand the medium and data integrity because these can effect outcomes. Next you organize the text, maybe by author or chronologically. Step 4 is feature extraction. This can be calculating sentiment or in our case extracting word tokens into various matrices. Step 5 is to perform some analysis. This course will help show you some basic analytical methods that can be applied to text. Lastly, step 6 is the one in which you hopefully answer your problem questions, reach an insight or conclusion, or in the case of predictive modeling produce an output. Now let’s learn about two approaches to text mining. The first is semantic parsing based on word syntax. In semantic parsing you care about word type and order. This method creates a lot of features to study. For example a single word can be tagged as part of a sentence, then a noun and also a proper noun or named entity. So that single word has three features associated with it. This effect makes semantic parsing "feature rich". To do the tagging, semantic parsing follows a tree structure to continually break up the text. In contrast, the bag of words method doesn’t care about word type or order. Here, words are just attributes of the document. In this example we parse the sentence "Steph Curry missed a tough shot". In the semantic example you see how words are broken down from the sentence, to noun and verb phrases and ultimately into unique attributes. Bag of words treats each term as just a single token in the sentence no matter the type or order. For this introductory course, we’ll focus on bag of words, but will cover more advanced methods in later courses! Let’s get a quick taste of text mining!
Views: 23495 DataCamp
02 HTML 5 Web Structure in Urdu with example
 
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In this video, You will learn about web structure in Urdu and Hindi. This is a part of HTML-5 tutorial for beginners course. If you want to learn Complete HTML-5 Course so Watch whole playlist and learn from it. Download my HTML-5 android app: https://play.google.com/store/apps/details?id=html.tutorial.urdu Complete HTML Course Playlist: https://www.youtube.com/watch?v=fs3wwqwp9DQ&list=PLlBXMnsxdNpY8xN-Cl4fq60JcsygZO6Mb Complete CSS Course Playlist: https://www.youtube.com/watch?v=FAxHKtnRXEg&list=PLlBXMnsxdNpYEWYacZCIX9dXIBd2U94Cr Complete JavaScript Course Playlist: https://www.youtube.com/watch?v=NsDvhMLp4fU&list=PLlBXMnsxdNpaKQKlzc3qhbRYMuLmioUHf Complete JQuery Course Playlist: https://www.youtube.com/watch?v=7wyKqNsfAlA&list=LLjASGhx9SQ9pB8Qk3jqa5Rw Complete PHP and MySQL Course Playlist: https://www.youtube.com/watch?v=_4RlQYNf-hQ&list=PLlBXMnsxdNpZ1uQ5s6N65Qs0usrZ85Z5q html 5 basic to advance,complete course,abdul aleem baig,free technology tutor,html 5 web structure,web structure,html tutorial in urdu,html tutorial in hindi,html tutorial in urdu/hindi,HTML,Introduction,HTML Urdu,HTML Hindi,HTML Training,HTML Tutorials,HTML Free,Abdulaleembaig,web structure in urdu,web structure mining,web structure design,web structure mining in hindi,web development
Views: 540 Abdul Aleem Baig
Introduction to Data Mining: Graph & Ordered Data
 
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Part three of data types, we introduce graph data and ordered data. And discuss the types of ordered data such as spatial-temporal and genomic data. -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8LkV0 See what our past attendees are saying here: https://hubs.ly/H0f8M270 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... -- Vimeo: https://vimeo.com/datasciencedojo
Views: 5554 Data Science Dojo
What is WEB CONTENT? What doe WEB CONTENT mean? WEB CONTENT meaning & explanation
 
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What is WEB CONTENT? What doe WEB CONTENT mean? WEB CONTENT meaning - WEB CONTENT definition - WEB CONTENT explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Web content is the textual, visual, or aural content that is encountered as part of the user experience on websites. It may include—among other things—text, images, sounds, videos, and animations. In Information Architecture for the World Wide Web, Lou Rosenfeld and Peter Morville write, "We define content broadly as 'the stuff in your Web site.' This may include documents, data, applications, e-services, images, audio and video files, personal Web pages, archived e-mail messages, and more. And we include future stuff as well as present stuff." While the Internet began with a U.S. Government research project in the late 1950s, the web in its present form did not appear on the Internet until after Tim Berners-Lee and his colleagues at the European laboratory (CERN) proposed the concept of linking documents with hypertext. But it was not until Mosaic, the forerunner of the famous Netscape Navigator, appeared that the Internet become more than a file serving system. The use of hypertext, hyperlinks, and a page-based model of sharing information, introduced with Mosaic and later Netscape, helped to define web content, and the formation of websites. Today, we largely categorize websites as being a particular type of website according to the content a website contains. Web content is dominated by the "page" concept, its beginnings in an academic setting, and in a setting dominated by type-written pages, the idea of the web was to link directly from one academic paper to another academic paper. This was a completely revolutionary idea in the late 1980s and early 1990s when the best a link could be made was to cite a reference in the midst of a type written paper and name that reference either at the bottom of the page or on the last page of the academic paper. When it was possible for any person to write and own a Mosaic page, the concept of a "home page" blurred the idea of a page. It was possible for anyone to own a "Web page" or a "home page" which in many cases the website contained many physical pages in spite of being called "a page". People often cited their "home page" to provide credentials, links to anything that a person supported, or any other individual content a person wanted to publish. Even though we may embed various protocols within web pages, the "web page" composed of "HTML" (or some variation) content is still the dominant way whereby we share content. And while there are many web pages with localized proprietary structure (most usually, business websites), many millions of websites abound that are structured according to a common core idea. Blogs are a type of website that contain mainly web pages authored in HTML (although the blogger may be totally unaware that the web pages are composed using HTML due to the blogging tool that may be in use). Millions of people use blogs online; a blog is now the new "home page", that is, a place where a persona can reveal personal information, and/or build a concept as to who this persona is. Even though a blog may be written for other purposes, such as promoting a business, the core of a blog is the fact that it is written by a "person" and that person reveals information from her/his perspective. Blogs have become a very powerful weapon used by content marketers who desire to increase their site's traffic, as well as, rank in the search engine result pages (SERPs). In fact, new research from Technorati shows that blogs now outrank social networks for consumer influence (Technorati’s 2013 Digital Influence Report data).
Views: 348 The Audiopedia
David Deng (ChemAxon): Extracting Chemical Information within Documents - from Desktop to Enterprise
 
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By providing reliable name to structure conversion, Naming has become the backbone of ChemAxon's chemical text mining tools, such as Document to Structure, JChem for SharePoint and chemicalize.org. In this presentation, a new addition to the text mining family, Document to Database will be introduced. Document to Database can continuously index chemical information from documents in a repository system (e.g. Documentum). Document to Database also provides a web interface, in which users can perform chemical search within the documents, or view the augmented documents with chemical information annotated. In addition to the Document to Database demonstration, the new improvements in Naming will also be covered, including Chinese chemical name recognition to accommodate the fast growing Chinese scientific literature; custom corporate ID to structure conversion via web service; and accuracy improvements.
Views: 323 ChemAxon
K mean clustering algorithm with solve example
 
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Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 321390 Last moment tuitions
KEYWORD SEARCH METHOD FOR UNSTRUCTURED,SEMI STRUCTURED & STRUCTURED DATA  TRAINING VIDEO,.
 
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Data mining and text analytics and noisy text analytics techniques are different methods used to find patterns in, or otherwise interpret, this information. Common techniques for structuring text usually involve manual tagging with metadata or Part-of-speech tagging for further text mining-based structuring. UIMA provides a common framework for processing this information to extract meaning and create structured data about the information. Software that creates machine-processable structure exploits the linguistic, auditory, and visual structure that is inherent in all forms of human communication.[5] This inherent structure can be inferred from text, for instance, by examining word morphology, sentence syntax, and other small- and large-scale patterns. Unstructured information can then be enriched and tagged to address ambiguities and relevancy-based techniques then used to facilitate search and discovery. Examples of "unstructured data" may include books, journals, documents, metadata, health records, audio, video, files, and unstructured text such as the body of an e-mail message, Web page, or word processor document. While the main content being conveyed does not have a defined structure, it generally comes packaged in objects (e.g. in files or documents, ...) that themselves have structure and are thus a mix of structured and unstructured data, but collectively this is still referred to as "unstructured data".[6] For example, an HTML web page is tagged, but HTML mark-up is typically designed solely for rendering. It does not capture the meaning or function of tagged elements in ways that support automated processing of the information content of the page. XHTML tagging does allow machine processing of elements although it typically does not capture or convey the semantic meaning of tagged terms. Since unstructured data is commonly found in electronic documents, the use of a content or document management system through which entire documents are categorized is often preferred over data transfer and manipulation from within the documents. Document management is thus the means to convey structure onto document collections. FOR MORE INFORMATION VISIT US AT http://www.seocertification.org.in/ http://www.seocertification.org.in/seo-training.php http://www.seocertification.org.in/sem-training.php http://www.seocertification.org.in/ppc.php http://www.seocertification.org.in/books.php http://www.seocertification.org.in/online-examination.php
Views: 366 seocertification
Learning to Extract Semantic Structure From Documents | Spotlight 3-1A
 
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Xiao Yang; Ersin Yumer; Paul Asente; Mike Kraley; Daniel Kifer; C. Lee Giles We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images. We consider document semantic structure extraction as a pixel-wise segmentation task, and propose a unified model that classifies pixels based not only on their visual appearance, as in the traditional page segmentation task, but also on the content of underlying text. Moreover, we propose an efficient synthetic document generation process that we use to generate pretraining data for our network. Once the network is trained on a large set of synthetic documents, we fine-tune the network on unlabeled real documents using a semi-supervised approach. We systematically study the optimum network architecture and show that both our multimodal approach and the synthetic data pretraining significantly boost the performance.
Chinese Name to Structure (CN2S) and Its Application in Chinese Text Mining
 
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Name to Structure (N2S) is a mature English name-to-structure conversion API development by ChemAxon. It is the underlying technology used in ChemAxon's chemical text mining tool D2S (Document to Structure). D2S can extract chemical information from individual file or a document repository system, such as Documentum and SharePoint. To accommodate the fast growing Chinese scientific literature, ChemAxon has recently developed CN2S (Chinese Name to Structure). In this presentation, we will demonstrate how CN2S can convert Chinese chemical names to structures, and its application in Chinese text mining.
Views: 266 ChemAxon
DATA MINING   2 Text Retrieval and Search Engines   Lesson 5 5 Web Indexing
 
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https://www.coursera.org/learn/text-retrieval
Views: 99 Ryo Eng
PDF Data Scraping
 
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Automated web scraping services provide fast data acquirement in structured format. No matter if used for big data, data mining, artificial intelligence, machine learning or business intelligence applications. The scraped data come from various sources and forms. It can be websites, various databases, XML feeds and CSV, TXT or XLS file formats for example. Billions of PDF files stored online form a huge data library worth scraping. Have you ever tried to get any data from various PDF files? Then you know how panful it is. We have created an algorithm that allows you to extract data in an easily readable structured way. With PDFix we can recognize all logical structures and we can give you a hierarchical structure of document elements in a correct reading order. With the PDFix SDK we believe your web crawler can be programmed to access the PDF files and: - Search Text inside PDFs – you can find and extract specific information - Detect and Export Tables - Extract Annotations - Detect and Extract Related Images - Use Regular Expression, Pattern Matching - Detect and Scrape information from Charts Structured format You will need the scraped data from PDFs in various formats. With the PDFix you will get a structured output in: - CSV - HTML - XML - JSON
Views: 192 Team PDFix
More Data Mining with Weka (2.4: Document classification)
 
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More Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 4: Document classification http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/QldvyV https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 7651 WekaMOOC
Mastering R Programming : Scraping Web Pages and Processing Texts | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2jDsrGS]. In this video, we'll take a look at how to scrape data from web pages and how to clean and process raw web and other textual data. • Show a web scraping example with rvest • Explain the structure of a typical webpage and basics of HTML and extract selector paths • Process and clean text data For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 3770 Packt Video
Comprehensive Extraction of Chemical Information from Text
 
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Key chemical information is locked within patents and internal documents. In this talk we will overview the chemical text mining provided by the combination of the Linguamatics I2E text mining platform with name-to-structure, substructure and similarity search from ChemAxon. We will describe how this combination of technologies allows us to address some of the most difficult challenges such as extraction of structure activity relationships from tables. To accommodate the fast growing scientific literature from Asia, ChemAxon recently added support for Chinese naming, and we will discuss the advantages of mining in the original language rather than in a machine translation.
Views: 154 ChemAxon
How does a blockchain work - Simply Explained
 
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What is a blockchain and how do they work? I'll explain why blockchains are so special in simple and plain English! 💰 Want to buy Bitcoin or Ethereum? Buy for $100 and get $10 free (through my affiliate link): https://www.coinbase.com/join/59284524822a3d0b19e11134 📚 Sources can be found on my website: https://www.savjee.be/videos/simply-explained/how-does-a-blockchain-work/ 🐦 Follow me on Twitter: https://twitter.com/savjee ✏️ Check out my blog: https://www.savjee.be ✉️ Subscribe to newsletter: https://goo.gl/nueDfz 👍🏻 Like my Facebook page: https://www.facebook.com/savjee
Views: 2517362 Simply Explained - Savjee
Lexis: An Optimization Framework for Discovering the Hierarchical Structure of Sequential Data
 
22:15
Author: Payam Siyari, Georgia Institute of Technology Abstract: Data represented as strings abounds in biology, linguistics, document mining, web search and many other fields. Such data often have a hierarchical structure, either because they were artificially designed and composed in a hierarchical manner or because there is an underlying evolutionary process that creates repeatedly more complex strings from simpler substrings. We propose a framework, referred to as Lexis, that produces an optimized hierarchical representation of a given set of “target” strings. The resulting hierarchy, “Lexis-DAG”, shows how to construct each target through the concatenation of intermediate substrings, minimizing the total number of such concatenations or DAG edges. The Lexis optimization problem is related to the smallest grammar problem. After we prove its NP-hardness for two cost formulations, we propose an efficient greedy algorithm for the construction of Lexis-DAGs. We also consider the problem of identifying the set of intermediate nodes (substrings) that collectively form the “core” of a Lexis-DAG, which is important in the analysis of Lexis-DAGs. We show that the Lexis framework can be applied in diverse applications such as optimized synthesis of DNA fragments in genomic libraries, hierarchical structure discovery in protein sequences, dictionary-based text compression, and feature extraction from a set of documents. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 115 KDD2016 video
Final Year Projects | Mining Frequent Subgraph Patterns from Uncertain Graph Data
 
06:25
Final Year Projects | Mining Frequent Subgraph Patterns from Uncertain Graph Data More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 1540 Clickmyproject
Scraping Web Page Data Automatically with Excel VBA
 
23:56
We learnt earlier how to scrape web data from web pages with Excel VBA using the inbuilt features of MS Excel and also from www.jobs.com which had a great html structure and allowed us to extract data quickly and easily. But how do you get data from a web page which has a difficult form with a button that has no id or name property? You need to adapt your VBA code to do the job. So we use the 'form submit' property and the 'td' elements of a table with a 'for loop' to make the data extraction smooth. Also note that some websites will make it difficult for you to extract the data from their web pages - for their own valid reasons. Hopefully many of your questions have been answered. And in the next video we'll see how we can get data from even more difficult web pages. Check for details here: http://www.familycomputerclub.com/scraping-web-page-data-automatically-with-excel-vba.html For more knowledge read the book Excel 2016 Power Programming with VBA: http://amzn.to/2kDP35V If you are from India you can get this book here: http://amzn.to/2jzJGqU
Views: 81350 Dinesh Kumar Takyar
Final Year Projects 2015 | Automated web usage data mining and recommendation system
 
08:26
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 539 Clickmyproject
Web scraping in Python (Part 4): Exporting a CSV with pandas
 
10:39
This is part 4 of an introductory web scraping tutorial. In this video, we'll use Python's pandas library to apply a tabular data structure to our scraped dataset and then export it to a CSV file. I'll end the video with advice and resources for getting better at web scraping. Watch the 4-video series: https://www.youtube.com/playlist?list=PL5-da3qGB5IDbOi0g5WFh1YPDNzXw4LNL == RESOURCES == Download the Jupyter notebook: https://github.com/justmarkham/trump-lies New York Times article: https://www.nytimes.com/interactive/2017/06/23/opinion/trumps-lies.html Beautiful Soup documentation: https://www.crummy.com/software/BeautifulSoup/bs4/doc/ pandas installation: http://pandas.pydata.org/pandas-docs/stable/install.html == DATA SCHOOL VIDEOS == Machine learning with scikit-learn: https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1 Data analysis with pandas: https://www.youtube.com/watch?v=yzIMircGU5I&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=1 Version control with Git: https://www.youtube.com/watch?v=xKVlZ3wFVKA&list=PL5-da3qGB5IBLMp7LtN8Nc3Efd4hJq0kD&index=1 == SUBSCRIBE FOR MORE VIDEOS == https://www.youtube.com/user/dataschool?sub_confirmation=1 == JOIN THE DATA SCHOOL COMMUNITY == Newsletter: http://www.dataschool.io/subscribe/ Twitter: https://twitter.com/justmarkham Facebook: https://www.facebook.com/DataScienceSchool/ Patreon: https://www.patreon.com/dataschool
Views: 20139 Data School
Faster Firestore via Data Aggregation
 
09:28
Retrieve data from Firestore in a way that is faster and more cost effective with data aggregation. In this episode, we use Firebase Cloud Functions to read data from a sub-collection and write it to its parent document. https://angularfirebase.com/lessons/firestore-cloud-functions-data-aggregation/ Firestore: https://firebase.google.com/docs/firestore/ NoSQL Aggregation: https://www.thoughtworks.com/insights/blog/nosql-databases-overview
Single and Multiple Document Summarization with Graph-based Ranking Algorithms
 
01:13:57
Graph-based ranking algorithms have been traditionally and successfully used in citation analysis, social networks, and the analysis of the link-structure of the World Wide Web. In short, these algorithms provide a way of deciding on the importance of a vertex within a graph, by taking into account global information recursively computed from the entire graph, rather than relying only on local vertex-specific information. In this talk, I will present an innovative unsupervised method for extractive summarization using graph-based ranking algorithms. I will describe several ranking algorithms, and show how they can be successfully applied to the task of automatic sentence extraction. The method was evaluated in the context of both a single and multiple document summarization task, with results showing improvement over previously developed state-of-the-art systems. I will also outline a number of other NLP applications that can be addressed with graph-based ranking algorithms, including word sense disambiguation, domain classification, and keyphrase extraction.
Views: 1329 Microsoft Research
Semi-unsupervised learning of taxonomic and non-taxonomic relationships from the web
 
01:01:08
Due to the size of the World Wide Web, it is necessary to develop tools for automatic or semi-automatic analyses of web data, such as finding patterns and implicit information in the web, a task usually known as Web Mining. In particular, web content mining consists of automatically mining data from textual web documents that can be represented with machine-readable semantic formalisms. While more traditional approaches to Information Extraction from text, such as those applied to the Message Understanding Conferences during the nineties, relied on small collections of documents with many semantic annotations, the characteristics of the web (its size, redundancy and the lack of semantic annotations in most texts) favor efficient algorithms able to learn from unannotated data. Furthermore, new types of web content such as web forums, blogs and wikis, are also a source of textual information that contain an underlying structure from which specialist systems can benefit. This talk will describe an ongoing project for automatically acquiring ontological knowledge (both taxonomic and non-taxonomic relationships) from the web in a partially unsupervised way. The proposed approach combines distributional semantics techniques with rote extractors. A particular focus will be set on an automatic addition of semantic tags to the Wikipedia with the aim of transforming it, with small effort, into a Semantic Wikipedia.
Views: 25 Microsoft Research
Mining Web Index Tutorial - How to view a quote reply on the Mining Web Index portal
 
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A request on the Mining Web Index portal goes to all the suppliers listed in that category. Those suppliers are able to quote on the request and broaden their client base. The Mining Web Index portal caters to the needs of mines throughout Africa.
Views: 51 Jason Jones
[PURDUE MLSS] Mining Heterogeneous Information Networks by Jiawei Han (Part 1/2)
 
01:30:36
Lecture notes: http://learning.stat.purdue.edu/mlss/_media/mlss/han.pdf Mining Heterogeneous Information Networks Multiple typed objects in the real world are interconnected, forming complex heterogeneous information networks. Different from some studies on social network analysis where friendship networks or web page networks form homogeneous information networks, heterogeneous information network reflect complex and structured relationships among multiple typed objects. For example, in a university network, objects of multiple types, such as students, professors, courses, departments, and multiple typed relationships, such as teach and advise are intertwined together, providing rich information. We explore methodologies on mining such structured information networks and introduce several interesting new mining methodologies, including integrated ranking and clustering, classification, role discovery, data integration, data validation, and similarity search. We show that structured information networks are informative, and link analysis on such networks becomes powerful at uncovering critical knowledge hidden in large networks. The tutorial also presents a few promising research directions on mining heterogeneous information networks. See other lectures at Purdue MLSS Playlist: http://www.youtube.com/playlist?list=PL2A65507F7D725EFB&feature=view_all
Views: 1091 Purdue University
Text Mining for Beginners
 
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This is a brief introduction to text mining for beginners. Find out how text mining works and the difference between text mining and key word search, from the leader in natural language based text mining solutions. Learn more about NLP text mining in 90 seconds: https://www.youtube.com/watch?v=GdZWqYGrXww Learn more about NLP text mining for clinical risk monitoring https://www.youtube.com/watch?v=SCDaE4VRzIM
Views: 75626 Linguamatics
This Sahara Railway Is One of the Most Extreme in the World | Short Film Showcase
 
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At more than 430 miles long, the Mauritania Railway has been transporting iron ore across the blistering heat of the Sahara Desert since 1963. ➡ Subscribe: http://bit.ly/NatGeoSubscribe ➡ Get More Short Film Showcase: http://bit.ly/ShortFilmShowcase About Short Film Showcase: The Short Film Showcase spotlights exceptional short videos created by filmmakers from around the web and selected by National Geographic editors. We look for work that affirms National Geographic's belief in the power of science, exploration, and storytelling to change the world. The filmmakers created the content presented, and the opinions expressed are their own, not those of National Geographic Partners. Know of a great short film that should be part of our Showcase? Email [email protected] to submit a video for consideration. See more from National Geographic's Short Film Showcase at http://documentary.com Get More National Geographic: Official Site: http://bit.ly/NatGeoOfficialSite Facebook: http://bit.ly/FBNatGeo Twitter: http://bit.ly/NatGeoTwitter Instagram: http://bit.ly/NatGeoInsta One of the longest and heaviest trains in the world, the 1.8-mile beast runs from the mining center of Zouerat to the port city of Nouadhibou on Africa’s Atlantic coast. The train is the bedrock of the Mauritanian economy and a lifeline to the outside world for the people who live along its route. Hop on board the ‘Backbone of the Sahara’ with filmmaker Macgregor for an incredible journey through the stunning Western Saharan landscape. Follow Macgregor: http://macgregor.works/ About National Geographic: National Geographic is the world's premium destination for science, exploration, and adventure. Through their world-class scientists, photographers, journalists, and filmmakers, Nat Geo gets you closer to the stories that matter and past the edge of what's possible. This Sahara Railway Is One of the Most Extreme in the World | Short Film Showcase https://youtu.be/jEo-ykjmHgg National Geographic https://www.youtube.com/natgeo
Views: 2936593 National Geographic
Introduction to R for Data Mining
 
01:00:34
Introduction to R for Data Mining
Views: 154 Timothy Kipkirui
√ Structure of a Web Page - Visual Text Types | English
 
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#iitutor #English # WebPageVisualTextTypes https://www.iitutor.com/ Webpages use a combination of text, images, layout and tone to communicate with their visitors. It uses links to send them in different directions. All of these things form the structure of the webpage. You need to discuss these pieces altogether as part of your answer. All websites communicate in some way; from those that provide information to those which you visit for pleasure. Websites are designed in a way to deliver content. Through a mix of design and content: You get communication. Technique terms will help when giving evidence of how a web page communicates. The more specific and detailed you can be, the better your answer. After all, “Bold, sharp text” sounds much better than “Really big words”. Tone is how the words and images suit the intended audience of the webpage. For instance, a website for a government department will have an authoritative tone. This essentially means no piano-playing cats in the banner. Layout refers to the way that the text and visuals on a website are arranged. It also is important to note things such as size and organisation, as they often are done in a way to make the most important features stand out. Visuals refer simply to the images used on the webpage. Visuals can either be icons, links or pictures which reflect the content and tone of the page. For instance, an anti-smoking website may use images of diseased body parts to reinforce the anti-smoking message. Text refers to two aspects: • What is written • The font and styling What is written can be referred to specifically using a direct quote from the web page. The font and styling may be used to highlight something important, or a link. Links are tabs or icons which direct a visitor to a website towards another part of it. It is often important to look at where the links that visitors to describe the purpose of the website.
Views: 91 iitutor.com
Text mining for chemical information: the ChiKEL project - David Milward (Linguamatics)
 
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As scientific and patent literature expands, we need more efficient ways to find and extract information. Text mining is already being used successfully to analyse sets of documents after they are found by structure search, in a two‐step process. Integrating name‐to‐structure and structure search directly within an interactive text mining system enables structure search to be mixed with linguistic constraints for more precise filtering. This talk will describe work done in partnership between ChemAxon and Linguamatics in the EU funded project, ChiKEL, including improvements made to name‐to‐structure software, how we evaluated this, and the approach taken to integrating name to structure within the text mining platform, I2E.
Views: 238 ChemAxon
What is CONCEPT MINING? What does CONCEPT MINING mean? CONCEPT MINING meaning & explanation
 
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What is CONCEPT MINING? What does CONCEPT MINING mean? CONCEPT MINING meaning - CONCEPT MINING definition - CONCEPT MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Concept mining is an activity that results in the extraction of concepts from artifacts. Solutions to the task typically involve aspects of artificial intelligence and statistics, such as data mining and text mining. Because artifacts are typically a loosely structured sequence of words and other symbols (rather than concepts), the problem is nontrivial, but it can provide powerful insights into the meaning, provenance and similarity of documents. Traditionally, the conversion of words to concepts has been performed using a thesaurus, and for computational techniques the tendency is to do the same. The thesauri used are either specially created for the task, or a pre-existing language model, usually related to Princeton's WordNet. The mappings of words to concepts are often ambiguous. Typically each word in a given language will relate to several possible concepts. Humans use context to disambiguate the various meanings of a given piece of text, where available machine translation systems cannot easily infer context. For the purposes of concept mining however, these ambiguities tend to be less important than they are with machine translation, for in large documents the ambiguities tend to even out, much as is the case with text mining. There are many techniques for disambiguation that may be used. Examples are linguistic analysis of the text and the use of word and concept association frequency information that may be inferred from large text corpora. Recently, techniques that base on semantic similarity between the possible concepts and the context have appeared and gained interest in the scientific community. One of the spin-offs of calculating document statistics in the concept domain, rather than the word domain, is that concepts form natural tree structures based on hypernymy and meronymy. These structures can be used to produce simple tree membership statistics, that can be used to locate any document in a Euclidean concept space. If the size of a document is also considered as another dimension of this space then an extremely efficient indexing system can be created. This technique is currently in commercial use locating similar legal documents in a 2.5 million document corpus. Standard numeric clustering techniques may be used in "concept space" as described above to locate and index documents by the inferred topic. These are numerically far more efficient than their text mining cousins, and tend to behave more intuitively, in that they map better to the similarity measures a human would generate.
Views: 440 The Audiopedia
KDD2016 paper 12
 
02:36
Title: Lexis: An Optimization Framework for Discovering the Hierarchical Structure of Sequential Data Authors: Payam Siyari*, Georgia Institute of Technology Bistra Dilkina, Georgia Institute of Technology\ Constantine Dovrolis, Georgia Institute of Technology Abstract: Data represented as strings abounds in biology, linguistics, document mining, web search and many other fields. Such data often have a hierarchical structure, either because they were artificially designed and composed in a hierarchical manner or because there is an underlying evolutionary process that creates repeatedly more complex strings from simpler substrings. We propose a framework, referred to as “Lexis”, that produces an optimized hierarchical representation of a given set of “target” strings. The resulting hierarchy, “Lexis-DAG”, shows how to construct each target through the concatenation of intermediate substrings, minimizing the total number of such concatenations or DAG edges. The Lexis optimization problem is related to the smallest grammar problem. After we prove its NP-Hardness for two cost formulations, we propose an efficient greedy algorithm for the construction of Lexis-DAGs. We also consider the problem of identifying the set of intermediate nodes (substrings) that collectively form the “core” of a Lexis-DAG, which is important in the analysis of Lexis-DAGs. We show that the Lexis framework can be applied in diverse applications such as optimized synthesis of DNA fragments in genomic libraries, hierarchical structure discovery in protein sequences, dictionary-based text compression, and feature extraction from a set of documents. More on http://www.kdd.org/kdd2016/ KDD2016 Conference will be recorded and published on http://videolectures.net/
Views: 2061 KDD2016 video
SVM-based Web Content Mining with Leaf Classification Unit from DOM-tree
 
09:49
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/clickmyproject Mail Us: [email protected]
Views: 74 Clickmyproject