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Apriori Algorithm (Associated Learning) - Fun and Easy Machine Learning
 
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Apriori Algorithm (Associated Learning) - Fun and Easy Machine Learning https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Limited Time - Discount Coupon Apriori Algorithm The Apriori algorithm is a classical algorithm in data mining that we can use for these sorts of applications (i.e. recommender engines). So It is used for mining frequent item sets and relevant association rules. It is devised to operate on a database containing a lot of transactions, for instance, items brought by customers in a store. It is very important for effective Market Basket Analysis and it helps the customers in purchasing their items with more ease which increases the sales of the markets. It has also been used in the field of healthcare for the detection of adverse drug reactions. A key concept in Apriori algorithm is that it assumes that: 1. All subsets of a frequent item sets must be frequent 2. Similarly, for any infrequent item set, all its supersets must be infrequent too. Support us on Patreon, so we can bring you more cool Machine and Deep Learning Content :) https://www.patreon.com/ArduinoStartups ------------------------------------------------------------ To learn more on Augmented Reality, IoT, Machine Learning FPGAs, Arduinos, PCB Design and Image Processing then Check out http://www.arduinostartups.com/ Please like and Subscribe for more videos :)
Views: 27814 Augmented Startups
Data Mining Lecture - - Finding frequent item sets | Apriori Algorithm | Solved Example (Eng-Hindi)
 
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In this video Apriori algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining in hindi, Finding frequent item sets, data mining, data mining algorithms in hindi, data mining lecture, data mining tools, data mining tutorial,
Views: 134468 Well Academy
Frequent Pattern Mining - Apriori Algorithm
 
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Here's a step by step tutorial on how to run apriori algorithm to get the frequent item sets. Recorded this when I took Data Mining course in Northeastern University, Boston.
Views: 67393 djitz
10. Understanding Program Efficiency, Part 1
 
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MIT 6.0001 Introduction to Computer Science and Programming in Python, Fall 2016 View the complete course: http://ocw.mit.edu/6-0001F16 Instructor: Prof. Eric Grimson In this lecture, Prof. Grimson introduces algorithmic complexity, a rough measure of the efficiency of a program. He then discusses Big "Oh" notation and different complexity classes. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 45850 MIT OpenCourseWare
The Apriori Algorithm ... How The Apriori Algorithm Works
 
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My web page: www.imperial.ac.uk/people/n.sadawi
Views: 154898 Noureddin Sadawi
Implementasi Algoritma FP-Growth menggunakan Phyton
 
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Tugas Data Mining dan Business Intelligence - Ilmu Komputer Universitas Gadjah Mada - Semester Ganjil Tahun Akademik 2015/2016 Anggota Kelompok : Brillianto Indrajaya 12/338104/PA/15094 Edo Syahputra 12/340063/PA/15114 Natanael Evan Tjandra 13/347452/PA/15248 Krisostomus Nova Rahmanto 13/347538/PA/15292 Carolus Gaza Nindra Tama 13/347460/PA/15250
Understanding Apriori Algorithm | Apriori Algorithm Using Mahout | Edureka
 
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Watch Sample Class Recording: http://www.edureka.co/mahout?utm_source=youtube&utm_medium=referral&utm_campaign=apriori-algo Apriori is an algorithm for frequent item set mining and association rule learning over transactional databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent item sets determined by Apriori can be used to determine association rules which highlight general trends in the database: this has applications in domains such as market basket analysis. This video gives you a brief insight of Apriori algorithm. Related Blogs: http://www.edureka.co/blog/introduction-to-clustering-in-mahout/?utm_source=youtube&utm_medium=referral&utm_campaign=apriori-algo http://www.edureka.co/blog/k-means-clustering/?utm_source=youtube&utm_medium=referral&utm_campaign=apriori-algo Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world. The topics related to ‘Apriori Algorithm’ have extensively been covered in our course ‘Machine Learning with Mahout’. For more information, please write back to us at [email protected] Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004
Views: 13463 edureka!
Fuzzy string matching using Python
 
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This video demonstrates the concept of fuzzy string matching using fuzzywuzzy in Python.
Views: 5398 Indian Pythonista
Apriori algorithm with complete solved example to find association rules
 
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Complete description of Apriori algorithm is provided with a good example. Apriori is an algorithm for frequent item set mining and association rule learning over transactional databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.
Views: 16931 StudyKorner
data mining fp growth | data mining fp growth algorithm | data mining fp tree example | fp growth
 
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In this video FP growth algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining algorithms in hindi, data mining in hindi, data mining lecture, data mining tools, data mining tutorial, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining fp growth, data mining fp growth algorithm, data mining fp tree example, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining, fp growth algorithm, fp growth algorithm example, fp growth algorithm in data mining, fp growth algorithm in data mining example, fp growth algorithm in data mining examples ppt, fp growth algorithm in data mining in hindi, fp growth algorithm in r, fp growth english, fp growth example, fp growth example in data mining, fp growth frequent itemset, fp growth in data mining, fp growth step by step, fp growth tree
Views: 91127 Well Academy
Text Analytics - Ep. 25 (Deep Learning SIMPLIFIED)
 
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Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics. Deep Learning TV on Facebook: https://www.facebook.com/DeepLearningTV/ Twitter: https://twitter.com/deeplearningtv Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While these models can be useful, they have some limitations. Language is subjective, and the same words can convey completely different meanings. Sometimes even synonyms can differ in their precise connotation. NLP applications require manual curation, and this labor contributes to variable quality and consistency. Deep Learning can be used to overcome some of the limitations of NLP. Unlike traditional methods, Deep Learning does not use the components of natural language directly. Rather, a deep learning approach starts by intelligently mapping each language component to a vector. One particular way to vectorize a word is the “one-hot” representation. Each slot of the vector is a 0 or 1. However, one-hot vectors are extremely big. For example, the Google 1T corpus has a vocabulary with over 13 million words. One-hot vectors are often used alongside methods that support dimensionality reduction like the continuous bag of words model (CBOW). The CBOW model attempts to predict some word “w” by examining the set of words that surround it. A shallow neural net of three layers can be used for this task, with the input layer containing one-hot vectors of the surrounding words, and the output layer firing the prediction of the target word. The skip-gram model performs the reverse task by using the target to predict the surrounding words. In this case, the hidden layer will require fewer nodes since only the target node is used as input. Thus the activations of the hidden layer can be used as a substitute for the target word’s vector. Two popular tools: Word2Vec: https://code.google.com/archive/p/word2vec/ Glove: http://nlp.stanford.edu/projects/glove/ Word vectors can be used as inputs to a deep neural network in applications like syntactic parsing, machine translation, and sentiment analysis. Syntactic parsing can be performed with a recursive neural tensor network, or RNTN. An RNTN consists of a root node and two leaf nodes in a tree structure. Two words are placed into the net as input, with each leaf node receiving one word. The leaf nodes pass these to the root, which processes them and forms an intermediate parse. This process is repeated recursively until every word of the sentence has been input into the net. In practice, the recursion tends to be much more complicated since the RNTN will analyze all possible sub-parses, rather than just the next word in the sentence. As a result, the deep net would be able to analyze and score every possible syntactic parse. Recurrent nets are a powerful tool for machine translation. These nets work by reading in a sequence of inputs along with a time delay, and producing a sequence of outputs. With enough training, these nets can learn the inherent syntactic and semantic relationships of corpora spanning several human languages. As a result, they can properly map a sequence of words in one language to the proper sequence in another language. Richard Socher’s Ph.D. thesis included work on the sentiment analysis problem using an RNTN. He introduced the notion that sentiment, like syntax, is hierarchical in nature. This makes intuitive sense, since misplacing a single word can sometimes change the meaning of a sentence. Consider the following sentence, which has been adapted from his thesis: “He turned around a team otherwise known for overall bad temperament” In the above example, there are many words with negative sentiment, but the term “turned around” changes the entire sentiment of the sentence from negative to positive. A traditional sentiment analyzer would probably label the sentence as negative given the number of negative terms. However, a well-trained RNTN would be able to interpret the deep structure of the sentence and properly label it as positive. Credits Nickey Pickorita (YouTube art) - https://www.upwork.com/freelancers/~0147b8991909b20fca Isabel Descutner (Voice) - https://www.youtube.com/user/IsabelDescutner Dan Partynski (Copy Editing) - https://www.linkedin.com/in/danielpartynski Marek Scibior (Prezi creator, Illustrator) - http://brawuroweprezentacje.pl/ Jagannath Rajagopal (Creator, Producer and Director) - https://ca.linkedin.com/in/jagannathrajagopal
Views: 39225 DeepLearning.TV
Association Rule Mining in R
 
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This video is using Titanic data file that's embedded in R (see here: https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/Titanic.html). You can find both the data and the code here: https://github.com/A01203249/YouTube-Videos.git. Use git clone to clone this repo locally and use the code.
Views: 44581 Ani Aghababyan
Association Rule Mining | Data Science | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) Watch the sample class recording: http://www.edureka.co/data-science?utm_source=youtube&utm_medium=referral&utm_campaign=association-rule-mining In data mining, association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using different measures of interestingness. Topics covered in the video are: 1. What is Association Rule Mining 2. Concepts in Association Rule Mining Related blogs: http://www.edureka.co/blog/application-of-clustering-in-data-science-using-real-life-examples/?utm_source=youtube&utm_medium=referral&utm_campaign=association-rule-mining http://www.edureka.co/blog/who-can-take-up-a-data-science-tutorial/?utm_source=youtube&utm_medium=referral&utm_campaign=association-rule-mining Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world. The topics related to ‘Association Rule Mining’ have been covered in our course ‘Data science’. For more information, please write back to us at [email protected]
Views: 27563 edureka!
How to Make a Text Summarizer - Intro to Deep Learning #10
 
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I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory. Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
Views: 127370 Siraj Raval
Data Mining  Association Rule - Basic Concepts
 
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short introduction on Association Rule with definition & Example, are explained. Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database. Parts of Association rule is explained with 2 measurements support and confidence. types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples. Names of Association rule algorithm and fields where association rule is used is also mentioned.
Apriori Algorithm with solved example|Find frequent item set in hindi | DWM | ML | BDA
 
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Sample Notes : https://drive.google.com/file/d/19xmuQO1cprKqqbIVKcd7_-hILxF9yfx6/view?usp=sharing for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho  :  https://goo.gl/85HQGm for full notes   please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made  notes of data warehouse and data mining  its only 200 rs once we get payment notification we will mail you the notes on your email id contact us at :[email protected] For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 120941 Last moment tuitions
Market Basket Analysis | Association Rules | R Programming | Data Prediction Algorithm
 
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In this video I've talked about the theory related to market basket analysis. Where I explained about its background and the components like support, confidence and lift. In the next video I'll talk about the code to achieve the association rules by applying market basket analysis in R.
Weka Text Classification for First Time & Beginner Users
 
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59-minute beginner-friendly tutorial on text classification in WEKA; all text changes to numbers and categories after 1-2, so 3-5 relate to many other data analysis (not specifically text classification) using WEKA. 5 main sections: 0:00 Introduction (5 minutes) 5:06 TextToDirectoryLoader (3 minutes) 8:12 StringToWordVector (19 minutes) 27:37 AttributeSelect (10 minutes) 37:37 Cost Sensitivity and Class Imbalance (8 minutes) 45:45 Classifiers (14 minutes) 59:07 Conclusion (20 seconds) Some notable sub-sections: - Section 1 - 5:49 TextDirectoryLoader Command (1 minute) - Section 2 - 6:44 ARFF File Syntax (1 minute 30 seconds) 8:10 Vectorizing Documents (2 minutes) 10:15 WordsToKeep setting/Word Presence (1 minute 10 seconds) 11:26 OutputWordCount setting/Word Frequency (25 seconds) 11:51 DoNotOperateOnAPerClassBasis setting (40 seconds) 12:34 IDFTransform and TFTransform settings/TF-IDF score (1 minute 30 seconds) 14:09 NormalizeDocLength setting (1 minute 17 seconds) 15:46 Stemmer setting/Lemmatization (1 minute 10 seconds) 16:56 Stopwords setting/Custom Stopwords File (1 minute 54 seconds) 18:50 Tokenizer setting/NGram Tokenizer/Bigrams/Trigrams/Alphabetical Tokenizer (2 minutes 35 seconds) 21:25 MinTermFreq setting (20 seconds) 21:45 PeriodicPruning setting (40 seconds) 22:25 AttributeNamePrefix setting (16 seconds) 22:42 LowerCaseTokens setting (1 minute 2 seconds) 23:45 AttributeIndices setting (2 minutes 4 seconds) - Section 3 - 28:07 AttributeSelect for reducing dataset to improve classifier performance/InfoGainEval evaluator/Ranker search (7 minutes) - Section 4 - 38:32 CostSensitiveClassifer/Adding cost effectiveness to base classifier (2 minutes 20 seconds) 42:17 Resample filter/Example of undersampling majority class (1 minute 10 seconds) 43:27 SMOTE filter/Example of oversampling the minority class (1 minute) - Section 5 - 45:34 Training vs. Testing Datasets (1 minute 32 seconds) 47:07 Naive Bayes Classifier (1 minute 57 seconds) 49:04 Multinomial Naive Bayes Classifier (10 seconds) 49:33 K Nearest Neighbor Classifier (1 minute 34 seconds) 51:17 J48 (Decision Tree) Classifier (2 minutes 32 seconds) 53:50 Random Forest Classifier (1 minute 39 seconds) 55:55 SMO (Support Vector Machine) Classifier (1 minute 38 seconds) 57:35 Supervised vs Semi-Supervised vs Unsupervised Learning/Clustering (1 minute 20 seconds) Classifiers introduces you to six (but not all) of WEKA's popular classifiers for text mining; 1) Naive Bayes, 2) Multinomial Naive Bayes, 3) K Nearest Neighbor, 4) J48, 5) Random Forest and 6) SMO. Each StringToWordVector setting is shown, e.g. tokenizer, outputWordCounts, normalizeDocLength, TF-IDF, stopwords, stemmer, etc. These are ways of representing documents as document vectors. Automatically converting 2,000 text files (plain text documents) into an ARFF file with TextDirectoryLoader is shown. Additionally shown is AttributeSelect which is a way of improving classifier performance by reducing the dataset. Cost-Sensitive Classifier is shown which is a way of assigning weights to different types of guesses. Resample and SMOTE are shown as ways of undersampling the majority class and oversampling the majority class. Introductory tips are shared throughout, e.g. distinguishing supervised learning (which is most of data mining) from semi-supervised and unsupervised learning, making identically-formatted training and testing datasets, how to easily subset outliers with the Visualize tab and more... ---------- Update March 24, 2014: Some people asked where to download the movie review data. It is named Polarity_Dataset_v2.0 and shared on Bo Pang's Cornell Ph.D. student page http://www.cs.cornell.edu/People/pabo/movie-review-data/ (Bo Pang is now a Senior Research Scientist at Google)
Views: 129140 Brandon Weinberg
Frequent Itemset Mining Projects | Frequent Itemset Mining Thesis
 
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Contact Best Java Projects Visit us: https://pythonprojects.net/
Views: 11 Python Projects
Regular Expressions (Regex) Tutorial: How to Match Any Pattern of Text
 
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In this regular expressions (regex) tutorial, we're going to be learning how to match patterns of text. Regular expressions are extremely useful for matching common patterns of text such as email addresses, phone numbers, URLs, etc. Almost every programming language has a regular expression library, so learning regular expressions with not only help you with finding patterns in your text editors, but also you'll be able to use these programming libraries to search for patterns programmatically as well. Let's get started... The code from this video can be found at: https://github.com/CoreyMSchafer/code_snippets/tree/master/Regular-Expressions Python Regex Tutorial: https://youtu.be/K8L6KVGG-7o If you enjoy these videos and would like to support my channel, I would greatly appreciate any assistance through my Patreon account: https://www.patreon.com/coreyms Or a one-time contribution through PayPal: https://goo.gl/649HFY If you would like to see additional ways in which you can support the channel, you can check out my support page: http://coreyms.com/support/ You can find me on: My website - http://coreyms.com/ Facebook - https://www.facebook.com/CoreyMSchafer Twitter - https://twitter.com/CoreyMSchafer Google Plus - https://plus.google.com/+CoreySchafer44/posts Tumblr - https://www.tumblr.com/blog/mycms
Views: 115576 Corey Schafer
APRIORI ALGORITHM EXAMPLE for computer science STUDENT in Machine Learning or DATA MINING
 
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its will help to computing background student. this topic will available in Data Mining as well as Machine Learning.Book one of the regular question asking in every Institution examination. HOW IT WORK Find all frequent itemsets: *Get frequent items: *Items whose occurrence in database is greater than or equal to the min.support threshold. *Get frequent itemsets: *Generate candidates from frequent items. *Prune the results to find the frequent itemsets. Generate strong association rules from frequent itemsets *Rules which satisfy the min.support and min.confidence threshold.
Machine Learning Lecture 3: working with text + nearest neighbor classification
 
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We continue our work with sentiment analysis from Lecture 2. I go over common ways of preprocessing text in Machine Learning: n-grams, stemming, stop words, wordnet, and part of speech tagging. In part 2 I introduce a common approach to k-nearest neighbor classification with text (It is very similar to something called the vector space model with tf-idf encoding and cosine distance) Code and other helpful links: http://karpathy.ca/mlsite/lecture3.php
Views: 24736 MLexplained
Hierarchical Clustering - Fun and Easy Machine Learning
 
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Hierarchical Clustering - Fun and Easy Machine Learning with Examples https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Hierarchical Clustering Looking at the formal definition of Hierarchical clustering, as the name suggests is an algorithm that builds hierarchy of clusters. This algorithm starts with all the data points assigned to a cluster of their own. Then two nearest clusters are merged into the same cluster. In the end, this algorithm terminates when there is only a single cluster left. The results of hierarchical clustering can be shown using Dendogram as we seen before which can be thought of as binary tree Difference between K Means and Hierarchical clustering Hierarchical clustering can’t handle big data well but K Means clustering can. This is because the time complexity of K Means is linear i.e. O(n) while that of hierarchical clustering is quadratic i.e. O(n2). In K Means clustering, since we start with random choice of clusters, the results produced by running the algorithm multiple times might differ. While results are reproducible in Hierarchical clustering. K Means is found to work well when the shape of the clusters is hyper spherical (like circle in 2D, sphere in 3D). K Means clustering requires prior knowledge of K i.e. no. of clusters you want to divide your data into. However with HCA , you can stop at whatever number of clusters you find appropriate in hierarchical clustering by interpreting the Dendogram. To learn more on Augmented Reality, IoT, Machine Learning FPGAs, Arduinos, PCB Design and Image Processing then Check out http://www.arduinostartups.com/ Please like and Subscribe for more videos :)
Views: 16254 Augmented Startups
Code | Market Basket Analysis | Association Rules | R Programming
 
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In my previous video I talked about the theory of Market basket analysis or association rules and in this video I have explained the code that you need to write to achieve the market basket analysis functionality in R. This will help you to develop your own market basket analysis or association rules application to mine the important rules which are present in the data.
R - Association Rules - Market Basket Analysis (part 1)
 
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Association Rules for Market Basket Analysis using arules package in R. The data set can be load from within R once you have installed and loaded the arules package. Association Rules are an Unsupervised Learning technique used to discover interesting patterns in big data that is usually unstructured as well.
Views: 50280 Jalayer Academy
Let’s Write a Decision Tree Classifier from Scratch - Machine Learning Recipes #8
 
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Hey everyone! Glad to be back! Decision Tree classifiers are intuitive, interpretable, and one of my favorite supervised learning algorithms. In this episode, I’ll walk you through writing a Decision Tree classifier from scratch, in pure Python. I’ll introduce concepts including Decision Tree Learning, Gini Impurity, and Information Gain. Then, we’ll code it all up. Understanding how to accomplish this was helpful to me when I studied Machine Learning for the first time, and I hope it will prove useful to you as well. You can find the code from this video here: https://goo.gl/UdZoNr https://goo.gl/ZpWYzt Books! Hands-On Machine Learning with Scikit-Learn and TensorFlow https://goo.gl/kM0anQ Follow Josh on Twitter: https://twitter.com/random_forests Check out more Machine Learning Recipes here: https://goo.gl/KewA03 Subscribe to the Google Developers channel: http://goo.gl/mQyv5L
Views: 139090 Google Developers
Association Rule Mining with R
 
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Association Rule Mining with R
Views: 357 Chuc Nguyen Van
Data Mining Preprocessing in Jupyter Notebook with Python
 
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Data Mining Preprocessing in Jupyter Notebook with Python using Pandas, Numpy and a Baseball dataset.
Views: 177 D Thomas
Ramachandran Outliers: Data Mining and Analysis using the Python Language
 
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David Vavrinak '18 delivers his presentation titled. "Ramachandran Outliers: Data Mining and Analysis using the Python Language" at Wabash College's 18th Annual Celebration of Student Research, Scholarship, and Creative Work.
Views: 49 Rob Shook
How DTW (Dynamic Time Warping) algorithm works
 
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In this video we describe the DTW algorithm, which is used to measure the distance between two time series. It was originally proposed in 1978 by Sakoe and Chiba for speech recognition, and it has been used up to today for time series analysis. DTW is one of the most used measure of the similarity between two time series, and computes the optimal global alignment between two time series, exploiting temporal distortions between them. Source code of graphs available at https://github.com/tkorting/youtube/blob/master/how-dtw-works.m The presentation was created using as references the following scientific papers: 1. Sakoe, H., Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoustic Speech and Signal Processing, v26, pp. 43-49. 2. Souza, C.F.S., Pantoja, C.E.P, Souza, F.C.M. Verificação de assinaturas offline utilizando Dynamic Time Warping. Proceedings of IX Brazilian Congress on Neural Networks, v1, pp. 25-28. 2009. 3. Mueen, A., Keogh. E. Extracting Optimal Performance from Dynamic Time Warping. available at: http://www.cs.unm.edu/~mueen/DTW.pdf
Views: 22584 Thales Sehn Körting
Apriori Algorithm with R Studio
 
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This is a video for RMD Sinhgad School of Engineering (BE-Computer) as a demonstration for one of the assignments of Business Analytics and Intelligence. Important Links: Ubuntu 16.04.2 LTS Download: https://www.ubuntu.com/download/desktop R installation instructions: https://www.datascienceriot.com/how-to-install-r-in-linux-ubuntu-16-04-xenial-xerus/kris/ R studio Download: https://www.rstudio.com/products/rstudio/download/ R Tutorial: http://tryr.codeschool.com/
Views: 5179 Varun Joshi
Frequent Pattern Mining Programming
 
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https://goo.gl/3dhnWH Programming assignment might take more time than written assignments, and it counts as 10% of your final grade, so please start early. This is an individual assignment, which means it is OK to discuss with your classmates and your TAs regarding the methods, but it is not OK to work together or share code. Similar libraries or programs of frequent pattern mining algorithms can be found on-line, but you are prohibited to use these resources directly, which means you can not include public libraries, or modify existing programs, since the purpose of this programming assignment is to help you go through frequent pattern mining processing step by step. You can use either Java/C++/Python as your programming language. You will use a package working under OS with Unix kernel. It works well on Linux/MacOS. If you are a Windows user, you need to either (1) connect to an EWS lab machine or (2) find other packages with the same function. You are asked to write a report about the assignment. So pay attention to the places with "Question to ponder" PaperID Title 7600 The Automatic Acquisition of Proof Methods 85825 Frequent pattern discovery with memory constraint For this assignment, we pre-processed the raw data by removing stop words, converting the words to lower cases and Objective Explore how frequent pattern mining can be applied to text mining to discover meaningful phrases. In this assignment, you will first run LDA on a corpus made up of titles from 5 domains' conference papers. Based on the results from LDA, a topic (representing a particular domain) is assigned to each word in each title. Then you write a frequent mining algorithm to mine frequent patterns from each topic to get meaningful phrases. The mined frequent patterns may not necessarily be *meaningful* phrases for the topic. So you will consider the question how to extract meaningful ones out of all the frequent patterns. The final goal is to output highly representative phrases for each topic. Step 1: Get to Know the Data We collect paper titles from conferences in computer science of 5 domains: Data Mining(DM), Machine Learning(ML), Database(DB), Information Retrieval(IR) and Theory(TH). You can download the raw data here paper_raw.txt. (Note that we will not use this file directly for this assignment. But you can look at this file to see what the original titles look like.) Each line contains two columns, PaperID and Title of a paper, separated by Tab('\t'). Recall the example in class. For each line in the file you can consider it as one transaction. Each word in the title is equivalent to an item in a transaction. Note that PaperID is unique in the whole data set. However, since this data is a subset of all the paper titles from a large corpus, PaperIDs are not starting from 0 and not consecutive. The raw data looks like this:
Views: 0 Nelson Waweru
Knuth–Morris–Pratt(KMP) Pattern Matching(Substring search)
 
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Pattern matching(substring search) using KMP algorithm https://www.facebook.com/tusharroy25 https://github.com/mission-peace/interview/blob/master/src/com/interview/string/SubstringSearch.java https://github.com/mission-peace/interview/wiki
Data Science Design Patterns
 
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Tennessee Leeuwenburg https://2016.pycon-au.org/schedule/78/view_talk Most 'data science' projects fall into just a few well-understood design patterns. This talk de-mystifies what those patterns are, how to use them practically, and how to get to grips with your data. We'll a look at how to understand the input/output structure of the models, how to design a reasonable 'experiment', and how to get started. We'll look at getting to grips with problems by simple data sets that can fit entirely on-screen, designing the basic 'form' of the machine before levelling up to bigger data and badder algorithms. All of this will be shown using Python tools, libraries and running code.
Views: 1083 PyCon Australia
Learning Python 07: Lists (arrays)
 
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For my Computer Science students
Views: 54 Paul Baumgarten
Python 3 Tutorial - Tuples
 
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This screencast introduces Python 3 tuples, multiple assignment/ return values, tuple unpacking and named tuples. This screencast was recorded using the ipython3 shell and Kazam screencaster on Kubuntu 14.04.
Graph Mining and Analysis  Lecture_12
 
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Graph Mining and Analysis Lecture_12 22 December 2015
Ben Chamberlain - Real time association mining in large social networks
 
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PyData London 2016 Social media can be used to perceive the relationships between individuals, companies and brands. Understanding the relationships between key entities is of vital importance for decision support in a swathe of industries. We present a real-time method to query and visualise regions of networks that could represent an industries, sports or political parties etc. There is a growing realisation that to combat the waning effectiveness of traditional marketing, social media platform owners need to find new ways to monetise their data. Social media data contains rich information describing how real world entities relate to each other. Understanding the allegiances, communities and structure of key entities is of vital importance for decision support in a swathe of industries that have hitherto relied on expensive, small scale survey data. We present a real-time method to query and visualise regions of networks that are closely related to a set of input vertices. The input vertices can define an industry, political party, sport etc. The key idea is that in large digital social networks measuring similarity via direct connections between nodes is not robust, but that robust similarities between nodes can be attained through the similarity of their neighbourhood graphs. We are able to achieve real-time performance by compressing the neighbourhood graphs using minhash signatures and facilitate rapid queries through Locality Sensitive Hashing. These techniques reduce query times from hours using industrial desktop machines to milliseconds on standard laptops. Our method allows analysts to interactively explore strongly associated regions of large networks in real time. Our work has been deployed in Python based software and uses the scipy stack (specifically numpy, pandas, scikit-learn and matplotlib) as well as the python igraph implementation. Slides available here: https://docs.google.com/presentation/d/1-NkcPM3XYn-7jk6233MvvFJiC5Abi3e2nGkF_NSFuFA/edit?usp=sharing Additional information: http://krondo.com/in-which-we-begin-at-the-beginning/
Views: 729 PyData
29 Python Regular Expressions | Python Tutorial | learn python scripting | Learnbay
 
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Regular expressions are used to solve a common problem: Given a string, determine whether that string matches a given pattern and, optionally, collect substrings that contain relevant information. Below topics are covered in this tutorial: 1: What is regular expression ? 2: What is pattern matching ? 3: What is re module ? 4: How to check Matching character in bracket , matching character not in bracket. 5: Basic Regular Expressions operations in detail. Course Feature: Course Features:- 1.Practical training and hands on Real Time Project Implementation With Assignment.Course includes mini projects/Real time industry level project with tons of assignment and exercises. 2.Expert Instructor Instructors from tier one product based MNC, from IITs/NITs 3.Placement Assistance: Interview Guidance And Placement assistance for job seekers. 4.Limited Batch Size. Limited Batch size for more personal attention during the course. 5.Project Support Real time project support. About the Course LearnBay's Python (Online/In-person) Certification Training (Novice to Expert). This course is for those For those - who wanted to build their career as a python developer and want to learn python for network automation, python for AWS , python for Data science, Python for automating test-cases and interested to get in-depth knowledge in python, This course will help you in becoming python expert, No prior knowledge of python is required to take up this course, even beneficial for those who are form non programming background and wanted to learn python to become data scientist. Teaching Python or any other technical training to those who are not from Technical background or form non coding (not from Computer Science ) background is always challenging, since we always have to teach them form basic and in more detail, It takes more time and effort, So keeping this challenge in mind Learnbay has design its course content and assignment in such a way that even harder concept can be explained in a very simple and clean way, Memory layout concept is very important for both who are from technical and non technical background, because if you can imagine the underlying memory layout of a program you can code it very easily irrespective of the language used. Where you will be learning all the basics and advance concept in python which includes, memory layout of a python program , python data structures like list, tuple, dictionary, you will be learning which data structure to use when, while solving real word problem.
Views: 5752 Learnbay
Design Patterns in Python #MP38
 
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Jean-Philippe Caissy and Rory Geoghegan present "Design Patterns in Python" Source: http://montrealpython.org/2013/06/mp38/
Views: 8046 Montreal-Python
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.