Cluster Analysis –Motivation, Theory & Practical Application

Analytics / Machine Learning / Data Science : Hierarchical & non hierarchical clustering (k-means), theory & sas program
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  • Lectures 33
  • Length 4 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
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    Available on iOS and Android
    Certificate of Completion
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About This Course

Published 2/2014 English

Course Description

  1. About the course - Cluster analysis is one of the most popular techniques used in data mining for marketing needs. The idea behind cluster analysis is to find natural groups within data in such a way that each element in the group is as similar to each other as possible. At the same time, the groups are as dissimilar to other groups as possible.
  2. Course materials- The course contains video presentations (power point presentations with voice), pdf, excel work book and sas codes.
  3. Course duration- The course should take roughly 7 hours to understand and internalize the concepts.
  4. Course Structure (contents) The structure of the course is as follows.


  • Where one applies cluster analysis. Why one should learn cluster analysis?
  • How it is different from objective segmentation (CHAID / CART )

Statistical foundation and practical application: Understand

  • Different type of cluster analysis
  • Cluster Analysis – high level view
  • Hierarchical clustering –
    • Agglomerative or Divisive technique
    • Dendogram – What it is? What does it show?
    • Scree plot - How to decide about number of clusters
    • How to use SAS command to run hierarchical clustering
    • When and why does on need to standardize the data?
    • How to understand and interpret the output
  • Non-hierarchical clustering (K means clustering).
    • Why do we need k means approach
    • How does it work?
    • How does it iterate?
    • How does it decide about combining old clusters?
    • How to use SAS command to run hierarchical clustering
    • When and why does on need to standardize the data?
    • How to understand and interpret the output

What are the requirements?

  • Basic understanding of statistics
  • Basic knowledge of SAS

What am I going to get from this course?

  • Learn cluster analysis in crystal clear and simple way
  • Learn hierarchical and non-hierarchical clustering
  • Know theory, business apllication, sas program and interpretation of output

Who is the target audience?

  • statistics and analytics professionals / students

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.


Section 1: Basic Understanding of Cluster Analysis
Intuitive Understanding of clusters
Difference between Cluster Analysis & Decision tree ( Objective segmentation)
Section 2: Motivation, Industry Applications & clustering as strategy. Industry Case study
Motivation to learn Clustering
Popular Industry Applications of Clustering
Clustering as strategy and Industry Case Study
PDF for lecture 1 to 5
20 pages
Section 3: Hierarchical Clustering
Section outline - what will be explained in this section?
Hierarchical Clustering High Level
Hierarchical Clustering Steps and Associated terms
How to get free access to SAS?
10 pages
Hierarchical Clustering Using SAS and Interpretation of The Output
Hierarchical Clustering Using Excel and explanation of SAS Output
Download files used?
Scree Plot - to decide optimal number of clusters
Why to standardize variables
Dendogram- The hierarchical structure
When to go for Non Hierarchical clustering
Section - pdf ( for lecture 7 to 15)
22 pages
Section 4: Non Hierarchical clustering - K means clustering
Section outline - what will be explained in this section
K means clustering alogorithm
Graphical Explanation of K means clustering
Hierarchical vs Non Hierarchical clustering
K means clustering for Data Mining
K means clustering using SAS

Please download the supplementary excl and see it along with video by pausing video time to time

SAS output Explanation pass 02
Section PDF - lecture 17 to 22
9 pages
Section 5: Variants of Hierarchical clustering, Different distance and linkage functions
Section Outline
Agglomerative and Divisive Hierarchical Clustering
Generic Distance formula
Different Linkage function
Section PDF (Lecture 26-29)
20 pages
How to download Excel files?
2 pages

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Instructor Biography

Gopal Prasad Malakar, Credit Card Analytics Professional - Trains on Data Mining

I am a seasoned Analytics professional with 15+ years of professional experience. I have industry experience of impactful and actionable analytics. I am a keen trainer, who believes that training is all about making users understand the concepts. If students remain confused after the training, the training is useless. I ensure that after my training, students (or partcipants) are crystal clear on how to use the learning in their business scenarios. My expertise is in Credit Card Business, Scoring (econometrics based model development), score management, loss forecasting and MS access based database application development.

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