Search results “Data mining classification and clustering”

Supervised and unsupervised learning algorithms

Views: 62647
Nathan Kutz

A tutorial about classification and prediction in Data Mining .

Views: 28303
Red Apple Tutorials

Complete set of Video Lessons and Notes available only at http://www.studyyaar.com/index.php/module/20-data-warehousing-and-mining
Data Mining, Classification, Clustering, Association Rules, Sequential Pattern Discovery, Regression, Deviation
http://www.studyyaar.com/index.php/module-video/watch/53-data-mining

Views: 85823
StudyYaar.com

Pattern Recognition by Prof. C.A. Murthy & Prof. Sukhendu Das,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit http://nptel.ac.in

Views: 20343
nptelhrd

Views: 12786
Educate Motivate

What is clustering
Partitioning a data into subclasses.
Grouping similar objects.
Partitioning the data based on similarity.
Eg:Library.
Clustering Types
Partitioning Method
Hierarchical Method
Agglomerative Method
Divisive Method
Density Based Method
Model based Method
Constraint based Method
These are clustering Methods or types.
Clustering Algorithms,Clustering Applications and Examples are also Explained.

Views: 88434
IT Miner - Tutorials,GK & Facts

Views: 780
Algorin Technical

Learn the basics of Machine Learning with R. Start our Machine Learning Course for free: https://www.datacamp.com/courses/introduction-to-machine-learning-with-R
First up is Classification. A *classification problem* involves predicting whether a given observation belongs to one of two or more categories. The simplest case of classification is called binary classification. It has to decide between two categories, or classes. Remember how I compared machine learning to the estimation of a function? Well, based on earlier observations of how the input maps to the output, classification tries to estimate a classifier that can generate an output for an arbitrary input, the observations. We say that the classifier labels an unseen example with a class.
The possible applications of classification are very broad. For example, after a set of clinical examinations that relate vital signals to a disease, you could predict whether a new patient with an unseen set of vital signals suffers that disease and needs further treatment. Another totally different example is classifying a set of animal images into cats, dogs and horses, given that you have trained your model on a bunch of images for which you know what animal they depict. Can you think of a possible classification problem yourself?
What's important here is that first off, the output is qualitative, and second, that the classes to which new observations can belong, are known beforehand. In the first example I mentioned, the classes are "sick" and "not sick". In the second examples, the classes are "cat", "dog" and "horse". In chapter 3 we will do a deeper analysis of classification and you'll get to work with some fancy classifiers!
Moving on ... A **Regression problem** is a kind of Machine Learning problem that tries to predict a continuous or quantitative value for an input, based on previous information. The input variables, are called the predictors and the output the response.
In some sense, regression is pretty similar to classification. You're also trying to estimate a function that maps input to output based on earlier observations, but this time you're trying to estimate an actual value, not just the class of an observation.
Do you remember the example from last video, there we had a dataset on a group of people's height and weight. A valid question could be: is there a linear relationship between these two? That is, will a change in height correlate linearly with a change in weight, if so can you describe it and if we know the weight, can you predict the height of a new person given their weight ? These questions can be answered with linear regression!
Together, \beta_0 and \beta_1 are known as the model coefficients or parameters. As soon as you know the coefficients beta 0 and beta 1 the function is able to convert any new input to output. This means that solving your machine learning problem is actually finding good values for beta 0 and beta 1. These are estimated based on previous input to output observations. I will not go into details on how to compute these coefficients, the function `lm()` does this for you in R.
Now, I hear you asking: what can regression be useful for apart from some silly weight and height problems? Well, there are many different applications of regression, going from modeling credit scores based on past payements, finding the trend in your youtube subscriptions over time, or even estimating your chances of landing a job at your favorite company based on your college grades.
All these problems have two things in common. First off, the response, or the thing you're trying to predict, is always quantitative. Second, you will always need input knowledge of previous input-output observations, in order to build your model. The fourth chapter of this course will be devoted to a more comprehensive overview of regression.
Soooo.. Classification: check. Regression: check. Last but not least, there is clustering. In clustering, you're trying to group objects that are similar, while making sure the clusters themselves are dissimilar.
You can think of it as classification, but without saying to which classes the observations have to belong or how many classes there are.
Take the animal photo's for example. In the case of classification, you had information about the actual animals that were depicted. In the case of clustering, you don't know what animals are depicted, you would simply get a set of pictures. The clustering algorithm then simply groups similar photos in clusters.
You could say that clustering is different in the sense that you don't need any knowledge about the labels. Moreover, there is no right or wrong in clustering. Different clusterings can reveal different and useful information about your objects. This makes it quite different from both classification and regression, where there always is a notion of prior expectation or knowledge of the result.

Views: 37315
DataCamp

Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-313488098/m-674518790
Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262
Georgia Tech online Master's program: https://www.udacity.com/georgia-tech

Views: 71748
Udacity

MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: John Guttag
Prof. Guttag discusses clustering.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Views: 75900
MIT OpenCourseWare

The k-means algorithm

Views: 18486
Nathan Kutz

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
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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: 322821
Last moment tuitions

Classification in Data Mining with classification algorithms. Explanation on classification algorithm the decision tree technique with Example.

Views: 36770
IT Miner - Tutorials,GK & Facts

Presentasi tugas matakuliah Data Mining kelompok 4, Mahasiswa Semester 5 Teknik Informatika Universitas Yudharta Pasuruan.
semoga bermanfaat...

Views: 916
HUMANIKA Universitas Yudharta Pasuruan

MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: John Guttag
Prof. Guttag introduces supervised learning with nearest neighbor classification using feature scaling and decision trees.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Views: 33064
MIT OpenCourseWare

How KNN algorithm works with example: K - Nearest Neighbor, Classifiers, Data Mining, Knowledge Discovery, Data Analytics

Views: 119449
shreyans jain

The k-nearest neighbors algorithm

Views: 12175
Nathan Kutz

Full lecture: http://bit.ly/K-means
The K-means algorithm starts by placing K points (centroids) at random locations in space. We then perform the following steps iteratively: (1) for each instance, we assign it to a cluster with the nearest centroid, and (2) we move each centroid to the mean of the instances assigned to it. The algorithm continues until no instances change cluster membership.

Views: 480704
Victor Lavrenko

Supervised and unsupervised learning algorithms

Views: 7833
Nathan Kutz

This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst.
You can check out the full details of the program here: https://www.udacity.com/course/nd002.

Views: 9080
Udacity

Clustering is the process of grouping the data into classes or clusters so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters.

Views: 8250
Red Apple Tutorials

K-Means Clustering Algorithm – Solved Numerical Question 1(Euclidean Distance)(Hindi)
Data Warehouse and Data Mining Lectures in Hindi

Views: 40082
Easy Engineering Classes

Classification consists of predicting a certain outcome based on a given input. In order to predict the outcome, the algorithm processes a training set containing a set of attributes and the respective outcome, usually called goal or prediction attribute. The algorithm tries to discover relationships between the attributes that would make it possible to predict the outcome. Next the algorithm is given a data set not seen before, called prediction set, which contains the same set of attributes, except for the prediction attribute – not yet known. The algorithm analyses the input and produces a prediction.

Views: 32234
Nina Canares

In the bayesian classification
The final ans doesn't matter in the calculation
Because there is no need of value for the decision you have to simply identify which one is greater and therefore you can find the final result.
-~-~~-~~~-~~-~-
Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3"
https://www.youtube.com/watch?v=GS3HKR6CV8E
-~-~~-~~~-~~-~-

Views: 153350
Well Academy

.
Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.
.

Views: 25743
Artificial Intelligence - All in One

Gaussian Mixture Models (GMM)

Views: 4120
Nathan Kutz

-~-~~-~~~-~~-~-
Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3"
https://www.youtube.com/watch?v=GS3HKR6CV8E
-~-~~-~~~-~~-~-

Views: 35198
Well Academy

Hierarchical Clustering - Fun and Easy Machine Learning with Examples
►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp
►KERAS Course - 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.
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Augmented Startups

Here are some of the most commonly used classification algorithms -- Logistic Regression, Naïve Bayes, Stochastic Gradient Descent, K-Nearest Neighbours, Decision Tree, Random Forest and Support Vector Machine.
https://analyticsindiamag.com/7-types-classification-algorithms/
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Views: 9499
Analytics India Magazine

KNN Classification– Solved Numerical Question in Hindi(Numerical 1)
K-Nearest Neighbour Classification Solved Numerical Problem
Data Warehouse and Data Mining Lectures in Hindi

Views: 29711
Easy Engineering Classes

This video is part of the Analyzing and Visualizing Data with Power BI course available on EdX.
To sign up for the course, visit: http://aka.ms/pbicourse.
To read more:
Power BI service https://aka.ms/pbis_gs
Power BI Desktop https://aka.ms/pbid_gs
Power BI basic concepts tutorial: https://aka.ms/power-bi-tutorial
To submit questions and comments about Power BI, please visit community.powerbi.com.
To submit questions and comments about Power BI, please visit community.powerbi.com.

Views: 20861
Microsoft Power BI

Enroll in the course for free at: https://bigdatauniversity.com/courses/machine-learning-with-python/
Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends.
This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.
This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.
Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!
Explore many algorithms and models:
Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.
Get ready to do more learning than your machine!
Connect with Big Data University:
https://www.facebook.com/bigdatauniversity
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https://www.linkedin.com/groups/4060416/profile
ABOUT THIS COURSE
•This course is free.
•It is self-paced.
•It can be taken at any time.
•It can be audited as many times as you wish.
https://bigdatauniversity.com/courses/machine-learning-with-python/

Views: 74112
Cognitive Class

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: 18491
Last moment tuitions

Demonstrating how to do Bayesian Classification, Nearest Neighbor, K means Clustering using WEKA . Generating data set and Probability Density Function using MATLAB.
Important links:
To know more about .arff formats go to: http://www.cs.waikato.ac.nz/ml/weka/arff.html
Data sets: http://repository.seasr.org/Datasets/UCI/arff/
Online matlab: http://octave-online.net/

Views: 32229
Niranjan Singh

Introduction
Data Mining deals with the discovery of hidden knowledge, unexpected patterns and new rules from large databases.
Crime analyses is one of the important application of data mining. Data mining contains many tasks and techniques including Classification, Association, Clustering, Prediction each of them has its own importance and applications
It can help the analysts to identify crimes faster and help to make faster decisions.
The main objective of crime analysis is to find the meaningful information from large amount of data and disseminates this information to officers and investigators in the field to assist in their efforts to apprehend criminals and suppress criminal activity.
In this project, Kmeans Clustering is used for crime data analysis.
Kmeans Algorithm
The algorithm is composed of the following steps:
It randomly chooses K points from the data set.
Then it assigns each point to the group with closest centroid.
It again recalculates the centroids.
Assign each point to closest centroid.
The process repeats until there is no change in the position of centroids.
Example of KMEANS Algorithm
Let’s imagine we have 5 objects (say 5 people) and for each of them we know two features (height and weight). We want to group them into k=2 clusters.
Our dataset will look like this:
First of all, we have to initialize the value of the centroids for our clusters. For instance, let’s choose Person 2 and Person 3 as the two centroids c1 and c2, so that c1=(120,32) and c2=(113,33).
Now we compute the Euclidean distance between each of the two centroids and each point in the data.

Views: 681
E2MATRIX RESEARCH LAB

KNIME is very helpful tool for Data Mining tasks like Clustering, Classification, Standard Deviation and Mean

Views: 29099
Sania Habib

Data Warehouse and Mining
For more: http://www.anuradhabhatia.com

Views: 78355
Anuradha Bhatia

The general idea behind clustering and examples in different fields.
This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining.
It is part of a series of 37 videos, all of which are available on YouTube.
For more information:
http://www.dataminingbook.com
https://www.twitter.com/gshmueli
https://www.facebook.com/dataminingbook
Here is the complete list of the videos:
• Welcome to Business Analytics Using Data Mining (BADM)
• BADM 1.1: Data Mining Applications
• BADM 1.2: Data Mining in a Nutshell
• BADM 1.3: The Holdout Set
• BADM 2.1: Data Visualization
• BADM 2.2: Data Preparation
• BADM 3.1: PCA Part 1
• BADM 3.2: PCA Part 2
• BADM 3.3: Dimension Reduction Approaches
• BADM 4.1: Linear Regression for Descriptive Modeling Part 1
• BADM 4.2 Linear Regression for Descriptive Modeling Part 2
• BADM 4.3 Linear Regression for Prediction Part 1
• BADM 4.4 Linear Regression for Prediction Part 2
• BADM 5.1 Clustering Examples
• BADM 5.2 Hierarchical Clustering Part 1
• BADM 5.3 Hierarchical Clustering Part 2
• BADM 5.4 K-Means Clustering
• BADM 6.1 Classification Goals
• BADM 6.2 Classification Performance Part 1: The Naive Rule
• BADM 6.3 Classification Performance Part 2
• BADM 6.4 Classification Performance Part 3
• BADM 7.1 K-Nearest Neighbors
• BADM 7.2 Naive Bayes
• BADM 8.1 Classification and Regression Trees Part 1
• BADM 8.2 Classification and Regression Trees Part 2
• BADM 8.3 Classification and Regression Trees Part 3
• BADM 9.1 Logistic Regression for Profiling
• BADM 9.2 Logistic Regression for Classification
• BADM 10 Multi-Class Classification
• BADM 11 Ensembles
• BADM 12.1 Association Rules Part 1
• BADM 12.2 Association Rules Part 2
• Neural Networks: Part I
• Neural Networks: Part II
• Discriminant Analysis (Part 1)
• Discriminant Analysis: Statistical Distance (Part 2)
• Discriminant Analysis: Misclassification costs and over-sampling (Part 3)

Views: 447
Galit Shmueli

How to work with images in Orange, what are image embeddings and how do perform clustering with embedded data.
For more information on image clustering, read the blog:
[Image Analytics: Clustering] https://blog.biolab.si/2017/04/03/image-analytics-clustering/
License: GNU GPL + CC
Music by: http://www.bensound.com/
Website: https://orange.biolab.si/
Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana

Views: 16908
Orange Data Mining

In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3.
This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy

Views: 389825
Thales Sehn Körting

Views: 21866
Prabhudev Konana

Views: 112
Sarhad Dawd

( Data Science Training - https://www.edureka.co/data-science )
This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Data Science playlist here: https://goo.gl/60NJJS
#kmeans #clusteranalysis #clustering #datascience #machinelearning
How it Works?
1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get Lifetime Access to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate!
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About the Course
Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
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Why Learn Data Science?
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
After the completion of the Data Science course, you should be able to:
1. Gain insight into the 'Roles' played by a Data Scientist
2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data Analysis Life Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
- - - - - - - - - - - - - -
Who should go for this course?
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies
For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).
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Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "

Views: 61147
edureka!

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