This is short tutorial for
What it is? (What do we mean by a cluster?)
How it is different from decision tree?
What is distance and linkage function?
What is hierarchical clustering?
What is scree plot & dendogram?
What is non hierarchical clustering (k-means)?
How to learn it in detail (step by step)?
---------------------------------
Read in great detail along with Excel output, computation and SAS code
----------------------------------
https://www.udemy.com/cluster-analysis-motivation-theory-practical-application/?couponCode=FB_CA_001

Views: 139181
Gopal Malakar

Definition,Measures , Application & Examples Cluster Analysis

Views: 308
Dr.Anamika Bhargava

.
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: 29413
Artificial Intelligence - All in One

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: 545397
Victor Lavrenko

Hierarchical Clustering - Fun and Easy Machine Learning with Examples
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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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Views: 35561
Augmented Startups

K-means clustering is used in all kinds of situations and it's crazy simple. Example R code in on the StatQuest website: https://statquest.org/2017/07/05/statquest-k-means-clustering/
For a complete index of all the StatQuest videos, check out:
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If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt...
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...or buying one or two of my songs (or go large and get a whole album!)
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...or just donating to StatQuest!
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Views: 85285
StatQuest with Josh Starmer

Let's detect the intruder trying to break into our security system using a very popular ML technique called K-Means Clustering! This is an example of learning from data that has no labels (unsupervised) and we'll use some concepts that we've already learned about like computing the Euclidean distance and a loss function to do this.
Code for this video:
https://github.com/llSourcell/k_means_clustering
Please Subscribe! And like. And comment. That's what keeps me going.
More learning resources:
http://www.kdnuggets.com/2016/12/datascience-introduction-k-means-clustering-tutorial.html
http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_ml/py_kmeans/py_kmeans_understanding/py_kmeans_understanding.html
http://people.revoledu.com/kardi/tutorial/kMean/
https://home.deib.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html
http://mnemstudio.org/clustering-k-means-example-1.htm
https://www.dezyre.com/data-science-in-r-programming-tutorial/k-means-clustering-techniques-tutorial
http://scikit-learn.org/stable/tutorial/statistical_inference/unsupervised_learning.html
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Views: 107400
Siraj Raval

The basic scenario is as follows: To extract a region coordinates from a 2D grid. A file in Arc Grid format first has header (attribute) information about the 2D grid and then followed by a grid itself. The value in each cell is the intensity of the area represented by that cell. If this value is zero then the area represented by that cell represent an empty area. Each connected set of cells with same intensity represents a region of that intensity. A region can have holes, this means that in an interior of a region there can be a cells of other intensity or intensity value zero. So, problem is extract each such region with a set of hole cycles.
Many approaches are available for the study of the data; these include representation of data in most defined form, reduction in noise, etc. While the various methods have been developed for the above mentioned purpose there still exist some complications. And sometimes these methods cannot be applied on all kind of data set; data set with varying noise, dimensions, variables. The focus of my project is to implement cluster algorithm and to validate the obtained result. This is particularly important for the protection of bad cluster formation and reduction of noise (irrelevant data objects).

Views: 123
K Brat

K-means sorts data based on averages. Dr Mike Pound explains how it works.
Fire Pong in Detail: https://youtu.be/ZoZMMg1r_Oc
Deep Dream: https://youtu.be/BsSmBPmPeYQ
FPS & Digital Video: https://youtu.be/yniSnYtkrwQ
Dr. Mike's Code:
% This script is the one mentioned during the Computerphile Image
% Segmentation video. I chose matlab because it's a popular tool for
% quickly prototyping things. Matlab licenses are pricey, if you don't have
% one (or, like me, work for an organisation that does) try Octave as a
% good free alternative. This code should work in Octave too.
% Load in an input image
im = imread('C:\Path\Of\Input\Image.jpg');
% In matlab, K-means operates on a 2D array, where each sample is one row,
% and the features are the columns. We can use the reshape function to turn
% the image into this format, where each pixel is one row, and R,G and B
% are the columns. We are turning a W,H,3 image into W*H,3
% We also cast to a double array, because K-means requires it in matlab
imflat = double(reshape(im, size(im,1) * size(im,2), 3));
% I specify that initialisation shuold sample points at
% random, rather than anything complex like kmeans++ initialisation.
% Kmeans++ takes a long time if you are using 256 classes.
% Perform k-means. This function returns the class IDs assigned to each
% pixel, and in this case we also want the mean values for each class -
% what colour is each class. This can take a long time if the value for K
% is large, I've used the sampling start strategy to speed things up.
% While KMeans is running, it will show you the iteration count, and the
% number of pixels that have changed class since last iteration. This
% number should get lower and lower, as the means settle on appropriate
% values. For large K, it's unlikely that we will ever reach zero movement
% (convergence) within 150 iterations.
K = 3
[kIDs, kC] = kmeans(imflat, K, 'Display', 'iter', 'MaxIter', 150, 'Start', 'sample');
% Matlab can output paletted images, that is, grayscale images where the
% colours are stored in a separate array. This array is kC, and kIDs are
% the grayscale indices.
colormap = kC / 256; % Scale 0-1, since this is what matlab wants
% Reshape kIDs back into the original image shape
imout = reshape(uint8(kIDs), size(im,1), size(im,2));
% Save file out, you need to subtract 1 from the image classes, since once
% stored in the file the values should go from 0-255, not 1-256 like matlab
% would do.
imwrite(imout - 1, colormap, 'C:\Path\Of\Output\Image.png');
http://www.facebook.com/computerphile
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This video was filmed and edited by Sean Riley.
Computer Science at the University of Nottingham: http://bit.ly/nottscomputer
Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com

Views: 185613
Computerphile

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: 93878
MIT OpenCourseWare

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: 101222
IT Miner - Tutorials & Travel

Views: 34001
Educate Motivate

This video is about KMedoid Clustering with NLP example

Views: 16974
Subalalitha Navaneethakrishnan

Título: Cluster analysis: application to molecular variability studies
Descripción: The objective of this Polimedia is to provide the student with the theorical basis of cluster analysis applied to variability studies with molecular markers. A concrete example is developed based on dominant molecular markers.
Autor/a: Pérez De Castro Ana María
+ Universitat Politècnica de València UPV: https://www.upv.es
+ Más vídeos en: https://www.youtube.com/valenciaupv
+ Accede a nuestros MOOC: https://upvx.es

Views: 3542
Universitat Politècnica de València - UPV

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: 14197
Red Apple Tutorials

In this example I am describing the process of clustering using the Pharmaceutical industry dataset.

Views: 12056
Michael Rechenthin

Provides illustration of doing cluster analysis with R.
R File: https://goo.gl/BTZ9j7
Machine Learning videos: https://goo.gl/WHHqWP
Includes,
- Illustrates the process using utilities data
- data normalization
- hierarchical clustering using dendrogram
- use of complete and average linkage
- calculation of euclidean distance
- silhouette plot
- scree plot
- nonhierarchical k-means clustering
Cluster analysis is an important tool related to analyzing big data or working in data science field.
Deep Learning: https://goo.gl/5VtSuC
Image Analysis & Classification: https://goo.gl/Md3fMi
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 111772
Bharatendra Rai

Supervised and unsupervised learning algorithms

Views: 71201
Nathan Kutz

Education, Medicine, Health, Healthy lifestyle, Physics, Chemistry, Maths, Nihilist

Views: 53
Nihilist

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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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: 35927
Last moment tuitions

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 #MachineLearning 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!
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https://bigdatauniversity.com/courses/machine-learning-with-python/

Views: 14389
Cognitive Class

** Python Training for Data Science: https://www.edureka.co/python **
This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) series presents another video on "K-Means Clustering Algorithm". Within the video you will learn the concepts of K-Means clustering and its implementation using python. Below are the topics covered in today's session:
1. What is Clustering?
2. Types of Clustering
3. What is K-Means Clustering?
4. How does a K-Means Algorithm works?
5. K-Means Clustering Using Python
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Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you:
1. Programmatically download and analyze data
2. Learn techniques to deal with different types of data – ordinal, categorical, encoding
3. Learn data visualization
4. Using I python notebooks, master the art of presenting step by step data analysis
5. Gain insight into the 'Roles' played by a Machine Learning Engineer
6. Describe Machine Learning
7. Work with real-time data
8. Learn tools and techniques for predictive modeling
9. Discuss Machine Learning algorithms and their implementation
10. Validate Machine Learning algorithms
11. Explain Time Series and its related concepts
12. Perform Text Mining and Sentimental analysis
13. Gain expertise to handle business in future, living the present
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Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain.
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Views: 47133
edureka!

In this video I walk you through how to run and interpret a hierarchical cluster analysis in SPSS and how to infer relationships depicted in a dendrogram. Here is a link to the data: https://drive.google.com/file/d/0B3T1TGdHG9aEbXBEMnZxQU43Qjg/view?usp=sharing

Views: 119713
James Gaskin

#kmean datawarehouse #datamining #lastmomenttuitions
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://lastmomenttuitions.com/course/data-warehouse/
Buy the Notes
https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/
if you have any query email us at
[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: 441891
Last moment tuitions

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Views: 16177
5 Minutes Engineering

Module XXII - CLUSTERING
CLUSTERING: Clustering is an important form of unsupervised learning (i.e., extracting patterns from unlabeled data). These two videos discuss how Kruskal's MST algorithm suggests flexible and useful greedy approaches to clustering problems.

Views: 264
intrigano

ACCESS the FULL COURSE here: https://academy.zenva.com/product/data-science-mini-degree/?zva_src=youtube-datascience-md
TRANSCRIPT
In this video, we are going to learn a little bit about cluster analysis. And this is a topic that we're gonna be discussing over the duration of this course. So just to give you an overview of the different things we're gonna be covering, I'm gonna give you an introduction to cluster analysis, basically what is it and what are the different applications of it, as well as what kind of algorithms can we expect. And in fact, we're gonna be covering three very popular algorithms, k-means clustering, DBSCAN, which stands for density-based spatial clustering of applications with noise, but usually we just call it DBSCAN, and then hierarchical agglomerative clustering, HAC. These are three very popular clustering algorithms. And the interesting thing is they all take very different approaches to creating clusters. And we're gonna get into all those in the subsequent videos. But first let's talk a little bit about cluster analysis. And that's what we're gonna be focusing on primarily in this video, just to acquaint you with some of the terminology as well as some applications of cluster analysis for example. So clustering analysis, so imagine we have some data. The whole point of clustering analysis is in an unsupervised way with no a priori information, we want to be able to separate different groups based on the data that we have. And now sometimes these groups are predefined. You have the set of data like in this case, and you say, well, this seems, we plot this data, and you say, well, it seems to fit into two little groups. Here there's a little clustering of points on the bottom left, and there's a larger, kind of elongated cluster on the top right and so we might say, well, we can give a predefined number of clusters. We want two clusters and we can give that to the clustering algorithms and then they'll group these guys together. It'll make a split and it actually, in some cases, we don't need to specify the number of clusters. In fact, some algorithms, which is DBSCAN, are actually smart enough to be able to figure out how many clusters are based entirely on the data. But algorithms like k-means will actually need to be specified how many clusters that we have. And so, for example, this data scan is actually taken, it's a very famous data set called the Iris Dataset, collected by Ronald Fisher, which is, and here is a quick historical side note, he's probably the most important statistician of the 20th century. A lot of statistical techniques that we have that are used in all kinds of companies were originally some of his work, but he collected this dataset of flowers. He has 50 different of three different kinds of species of flowers and he plots their measured properties like petal width, petal length, sepal width, and sepal length, and they're all plotted out. In this case, what I've actually done is removed the class labels, because usually when we're doing clustering analysis, we don't have the correct labels. In fact, that's what the clustering is trying to give us. It's trying to give us some notion that these things belong together and these other things belong together. So this is just a kind of data that you might expect with some clustering. So clustering is taking our data and then putting it into groups such that the groups have some kind of similar properties or similar attributes here. So if we go back a slide here, so we have one cluster at the bottom left for example. That might be considered a cluster where the flowers in that cluster have a small petal length and a smaller petal width, for example. That's an example of grouping, as I'm talking about. And there's so many different applications of clustering analysis, not just used for something like data science. But also things like medical imaging for things like x-rays or MRIs or FMRIs. They use clustering analysis.
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Views: 38
Zenva

This K Means clustering algorithm tutorial video will take you through machine learning basics, types of clustering algorithms, what is K Means clustering, how does K Means clustering work with examples along with a demo in python on K-Means clustering - color compression. This Machine Learning algorithm tutorial video is ideal for beginners to learn how K Means clustering work.
Below topics are covered in this K-Means Clustering Algorithm Tutorial:
1. Types of Machine Learning? ( 07:08 )
2. What is K Means Clustering? ( 00:10 )
3. Applications of K Means Clustering ( 09:27 )
4. Common distance measure ( 10:20 )
5. How does K Means Clustering work? ( 12:27 )
6. K Means Clustering Algorithm ( 20:08 )
7. Demo In Python: K Means Clustering ( 26:20 )
8. Use case: Color compression In Python ( 38:38 )
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
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To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Kmeans-Clustering-Algorithm-Xvwt7y2jf5E&utm_medium=Tutorials&utm_source=youtube
#MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse
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Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
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By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
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3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
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We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
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Simplilearn

#ClusterAnalysis | A tutorial on Cluster Analysis using real-life examples. Learn the objective of cluster analysis, the methodology used and interpreting results from the same.
Cluster analysis is an exploratory data analysis tool which aims at sorting different objects into groups in a way that the degree of association between two objects is maximal if they belong to the same group and minimal otherwise.
Clustering methods can be classified into the following categories −
- Partitioning Method
- Hierarchical Clustering
- Density-based Method
- Grid-Based Method
- Model-Based Method
- Constraint-based Method
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Views: 33548
Great Learning

To find groups of parallel lines in an image with noise DBSCAN is a good algorithm to postprocess hough lines data. Qt, matplotlib enable us to understand the calculated data and support the fine tuning.

Views: 740
microelly

Cluster is a unsupervised learning algorithm used for modelling unlabeled data.
Supervised learning: discover patterns in the data that relate data attributes with a target (class) attribute.
These patterns are then utilized to predict the values of the target attribute in future data instances.
Unsupervised learning: The data have no target attribute.
We want to explore the data to find some intrinsic structures in them.
Clustering is a technique for finding similarity groups in data, called clusters. I.e.,
it groups data instances that are similar to (near) each other in one cluster and data instances that are very different (far away) from each other into different clusters.
Clustering is often called an unsupervised learning task
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Analytics University

Learn the basics of Cluster Analysis using real-life examples. Know more about the objective of cluster analysis, the methodology used and interpreting results from the same.
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#ClusterAnalysis #WhatIsClusterAnalysis #Tutorial #GreatLearning #GreatLakes
About Great Learning:
- Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more.
- Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM
- What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U
- Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670
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Great Learning has collaborated with the University of Texas at Austin for the PG Program in Artificial Intelligence and Machine Learning and with UT Austin McCombs School of Business for the PG Program in Analytics and Business Intelligence.

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Great Learning

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: 40392
DataCamp

Attribute data and relationship data are two principle types of data, representing the intrinsic and extrinsic properties of entities. While attribute data has been the main source of data for cluster analysis, relationship data such as social networks or metabolic networks are becoming increasingly available. In many cases these two data types carry complementary information, which calls for a joint cluster analysis of both data types in order to achieve more natural clusterings. For example, when identifying research communities, relationship data could represent co-author relationships and attribute data could represent the research interests of scientists. Communities could then be identified as clusters of connected scientists with similar research interests. Our introduction of joint cluster analysis is part of a recent, broader trend to consider as much background information as possible in the process of cluster analysis, and in general, in data mining. In this talk, we briefly review related work including constrained clustering, semi-supervised clustering and multi-relational clustering. We then propose the Connected k-Center (CkC) problem, which aims at finding k connected clusters minimizing the radius with respect to the attribute data. We sketch the main ideas of the proof of NP-completeness and present a constant factor approximation algorithm for the CkC problem. Since this algorithm does not scale to large datasets, we have also developed NetScan, a heuristic algorithm that is efficient for large, real databases. We report experimental results from two applications, community identification and document clustering, both based on DBLP data. Our experiments demonstrate that NetScan finds clusters that are more meaningful and accurate than the results of existing algorithms. We conclude the talk with other promising applications and new problems of joint cluster analysis. In particular, we discuss the clustering of gene expression data and the hotspot analysis of crime data as well as a joint cluster analysis problem that does not require the user to specify the number of clusters in advance.

Views: 50
Microsoft Research

BigData COE offers interactive online classes and Provide Live Case Studies to help you understand the subject by the certified professionals

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Bigdata Coe

IEEE CIS Cyprus Chapter, Region 8
Date: 31 October 2012
Time: 17:00 -- 19:00
Location: University of Cyprus, Cyprus
Presentation: http://www.slideshare.net/ieee_cis_cyprus/prof-jim-bezdek-every-picture-tells-a-story-visual-cluster-analysis
The talk overviews the history of Visual Clustering, which began thousands of years ago. The first image for this appeared in 1873. Three algorithms for visual assessment of clustering tendency examined, namely the VAT, iVAT and asiVAT, with applications to social network analysis. Particularly three applications, one for each algorithm will be discussed: time series analysis with clusters of linguistic medoid prototypes in Eldercare data (iVAT); social network analysis with Sampson's Monastery data (asiVAT); and network access security (VAT), a commercial application developed by CA technologies.
Help us caption & translate this video!
http://amara.org/v/CjLm/

Views: 822
CIS Cyprus

This talk was given by Johannes Fuchs at the Eurographics 2019 conference in Genoa, Italy on 8 May 2019.

Views: 20
DataVisBob Laramee

The main ideas behind PCA are actually super simple and that means it's easy to interpret a PCA plot: Samples that are correlated will cluster together apart from samples that are not correlated with them. In this video, I walk through the ideas so that you will have an intuitive sense of how PCA plots are draw. If you'd like more details, check out my full length PCA video here: https://youtu.be/_UVHneBUBW0
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StatQuest with Josh Starmer

Samory Kpotufe, Princeton University
Estimating the mode or modal-sets (i.e. extrema points or surfaces) of an unknown density from sample is a basic problem in data analysis. Such estimation is relevant to other problems such as clustering, outlier detection, or can simply serve to identify low-dimensional structures in high dimensional-data (e.g. point-cloud data from medical-imaging, astronomy, etc). Theoretical work on mode-estimation has largely concentrated on understanding its statistical difficulty, while less attention has been given to implementable procedures. Thus, theoretical estimators, which are often statistically optimal, are for the most part hard to implement. Furthermore for more general modal-sets (general extrema of any dimension and shape) much less is known, although various existing procedures (e.g. for manifold-denoising or density-ridge estimation) have similar practical aim. I’ll present two related contributions of independent interest: (1) practical estimators of modal-sets – based on particular subgraphs of a k-NN graph – which attain minimax-optimal rates under surprisingly general distributional conditions; (2) high-probability finite sample rates for k-NN density estimation which is at the heart of our analysis. Finally, I’ll discuss recent successful work towards the deployment of these modal-sets estimators for various clustering applications.
Much of the talk is based on a series of work with collaborators S. Dasgupta, K. Chaudhuri, U. von Luxburg, and Heinrich Jiang.

Views: 293
Center for Brains, Minds and Machines (CBMM)