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: 90140
IT Miner - Tutorials,GK & Facts

.
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: 26340
Artificial Intelligence - All in One

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: 19267
Last moment tuitions

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: 80264
MIT OpenCourseWare

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: 334178
Last moment tuitions

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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Augmented Startups

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: 869
E2MATRIX RESEARCH LAB

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: 494437
Victor Lavrenko

This video is about KMedoid Clustering with NLP example

Views: 10932
Subalalitha Navaneethakrishnan

( 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
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Check our complete Data Science playlist here: https://goo.gl/60NJJS
#kmeans #clusteranalysis #clustering #datascience #machinelearning
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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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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.
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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
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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:
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Views: 63633
edureka!

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: 8779
Red Apple Tutorials

In this Machine Learning & Python video tutorial I demonstrate Hierarchical Clustering method.
Hierarchical Clustering is a part of Machine Learning and belongs to Clustering family:
- Connectivity-based clustering (hierarchical clustering)
- Centroid-based clustering (K-Means Clustering) - https://www.youtube.com/watch?v=iybATqk6LNI
- Distribution-based clustering
- Density-based clustering
In data mining and statistics, Hierarchical Clustering also called hierarchical cluster analysis or HCA is a method of cluster analysis which seeks to build a hierarchy of clusters. In this video I demonstrate how Agglomerative Hierarchical Clustering is working.
Must know for Hierarchical Clustering is knowing Dendrograms. Dendrogram helps you to decide the optimal number of clusters for your dataset.
For executing task in Python I used:
- sklearn library that is for Machine Learning algorithms.
- ward method that means Minimum Variance Method.
If you are interesting more in Hierarchical Clustering, read my article on LinkedIn where I described my experiment about combining Machine Learning (Hierarchical Clustering) in GIS (Geographical Information System). - https://www.linkedin.com/pulse/machine-learning-gis-hierarchical-clustering-urban-bielinskas
Data-set for this example is taken from https://www.kaggle.com. There you can find many dataset for very different Machine Learning tasks.
Hierarchicaal Clustering is very usable in solving Data Analysis, Data Mining and Statistics problems.
If you have any question or comments please write below.
Do not forget to subscribe me if want to follow my new videos about Machine Learning, Data Science, Python programming and relative issues.
Follow me on LinkedIn: https://www.linkedin.com/in/bielinskas/

Views: 3225
Dr. Vytautas Bielinskas

Application for the project of Analysis of Chicago City Crime Data using Data mining for The University of Oklahoma class CS - 5593
0:00 Clustering application
5:37 Classification Application
Members of the group:
Cristian Paez
Pravallika Uppuganti
Ryan Kiel

Views: 1073
Cristian Paez

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:
www.dataminingbook.com
twitter.com/gshmueli
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 Nets: Part II
• Discriminant Analysis (Part 1)
• Discriminant Analysis: Statistical Distance (Part 2)
• Discriminant Analysis: Misclassification costs and over-sampling (Part 3)

Views: 2904
Galit Shmueli

SImplest Video about density based algorithm - DBSCAN

Views: 34734
Red Apple Tutorials

Explained K means Clustering Algorithm With Best Example In Quickest And Easiest way Ever in Hindi.
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5 Minutes Engineering

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: 11762
Cognitive Class

Views: 12916
Machine Learning- Sudeshna Sarkar

A tutorial about classification and prediction in Data Mining .

Views: 29787
Red Apple Tutorials

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: 132320
Gopal Malakar

Here we discuss DBSCAN which is one of the method that uses Density based clustering method. Here we discuss the Algorithm, shows some examples and also give advantages and disadvantages of DBSCAN.
The url of dbscan in python : http://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html

Views: 16123
Machine Learning - CTW

** 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
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
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1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work
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3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate!
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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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Why learn Python?
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.
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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Views: 28270
edureka!

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.
Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1
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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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A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
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Why learn Machine Learning?
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
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
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What skills will you learn from this Machine Learning course?
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.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
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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Who should take this Machine Learning Training Course?
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
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
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Views: 20039
Simplilearn

These Videos Will Make You To Perfect In Data Mining Introduction And Applications Of Data Mining
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Follow Me On Facebook : https://www.facebook.com/narayanaitechnologies

Views: 2251
Narayana i Technologies

Please feel free to get in touch with me :)
If it helped you, please like my facebook page and don't forget to subscribe to Last Minute Tutorials. Thaaank Youuu.
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For any queries or suggestions, kindly mail at: [email protected]

Views: 86646
Last Minute Tutorials

Simple overview of data mining with R and RStudio.

Views: 3115
Gaurav Jetley

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.

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DataCamp

MSBI - SSAS - Data Mining - SEQUENCE CLUSTERING

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M R Dhandhukia

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.

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IT Miner - Tutorials,GK & Facts

Includes a brief introduction to credit card fraud, types of credit card fraud, how fraud is detected, applicable data mining techniques, as well as drawbacks.

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Ben Rodick

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

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Anuradha Bhatia

More Data Mining with Weka: online course from the University of Waikato
Class 3 - Lesson 6: Evaluating clusters
http://weka.waikato.ac.nz/
Slides (PDF):
http://goo.gl/nK6fTv
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 21144
WekaMOOC

This is a walkthrough of the IBM weka tutorials covering regression and clustering
https://www.ibm.com/developerworks/library/os-weka1/
https://www.ibm.com/developerworks/library/os-weka2/
https://www.ibm.com/developerworks/library/os-weka3/

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jengolbeck

Order my books at 👉 http://www.tek97.com/ #RanjiRaj #DataMining #BIRCH
Follow me on Instagram 👉 https://www.instagram.com/reng_army/
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Support my work on Patreon 👉 https://www.patreon.com/ranjiraj
BIRCH is a technique used for clustering in data mining sets for scalable clusters. Watch now!
BIRCH هي تقنية تستخدم لتجميع مجموعات بيانات التعدين للمجموعات القابلة للتوسع. شاهد الآن !
BIRCH é uma técnica usada para cluster em conjuntos de mineração de dados para clusters escalonáveis. Assista agora !
BIRCH - это метод, используемый для кластеризации в наборах интеллектуального анализа данных для масштабируемых кластеров. Смотри !
BIRCH ist eine Technik, die zum Clustering in Data Mining-Sets für skalierbare Cluster verwendet wird. Schau jetzt !
BIRCH est une technique utilisée pour la mise en cluster dans des ensembles d'exploration de données pour des clusters évolutifs. Regarde maintenant !
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