Cluster Analysis- Theory & workout using SAS and R

Course Curriculum

Chapter 1: Overall structure of the course

Lecture 1: Course details – what is in four parts

Chapter 2: Part 01 – Cluster Analysis using SAS

Lecture 1: What is covered in part 01 – cluster analysis using SAS

Lecture 2: Intuitive Understanding of clusters

Lecture 3: Difference between Cluster Analysis & Decision tree ( Objective segmentation)

Chapter 3: Motivation, Industry Applications & clustering as strategy. Industry Case study

Lecture 1: Motivation to learn Clustering

Lecture 2: Popular Industry Applications of Clustering

Lecture 3: Clustering as strategy and Industry Case Study

Lecture 4: PDF for above lectures

Chapter 4: Hierarchical Clustering

Lecture 1: Section outline – what will be explained in this section?

Lecture 2: Hierarchical Clustering High Level

Lecture 3: Hierarchical Clustering Steps and Associated terms

Lecture 4: How to get free access to SAS?

Lecture 5: Hierarchical Clustering Using SAS and Interpretation of The Output

Lecture 6: Hierarchical Clustering Using Excel and explanation of SAS Output

Lecture 7: Download resources files (Excel).

Lecture 8: Scree Plot – to decide optimal number of clusters

Lecture 9: Why to standardize variables

Lecture 10: Dendrogram- The hierarchical structure

Lecture 11: When to go for Non Hierarchical clustering

Lecture 12: Section – pdf

Chapter 5: Non Hierarchical clustering – K means clustering

Lecture 1: Section outline – what will be explained in this section

Lecture 2: K means clustering alogorithm

Lecture 3: Graphical Explanation of K means clustering

Lecture 4: Hierarchical vs Non Hierarchical clustering

Lecture 5: K means clustering for Data Mining

Lecture 6: K means clustering using SAS

Lecture 7: SAS output Explanation pass 01

Lecture 8: SAS output Explanation pass 02

Lecture 9: Section PDF

Lecture 10: Section FAQ – Non Hierarchical Clustering

Chapter 6: Variants of Hierarchical clustering, Different distance and linkage functions

Lecture 1: Section Outline

Lecture 2: Agglomerative and Divisive Hierarchical Clustering

Lecture 3: Generic Distance formula

Lecture 4: Different Linkage function

Lecture 5: Section PDF

Lecture 6: How to download Excel files?

Chapter 7: Part 02- cluster Analysis using R

Lecture 1: Introduction to Cluster Analysis Using R

Lecture 2: Details of Hierarchical clustering Function in R

Lecture 3: Demo of Hierarchical clustering using R

Lecture 4: Scree plot for hierarchical clustering in R

Lecture 5: Details of Non Hierarchical clustering Function in R

Lecture 6: Demo of Non Hierarchical clustering using R

Chapter 8: Part 03 – Cluster Analysis in data mining scenario (industrial set up)

Lecture 1: Section Overview

Lecture 2: Dealing with Nominal Categorical Variable

Lecture 3: Dealing with Ordinal Categorical Variable

Lecture 4: Dealing with Missing Value of a Numeric Variable

Lecture 5: Outlier detection n Treatment

Lecture 6: Standardize Numeric Variable

Lecture 7: Select Numeric Variables by Variable Clustering

Lecture 8: Iterate for final clusters

Lecture 9: Business Presentation of cluster solution

Chapter 9: Demo of clustering approach for data mining scenario using R

Lecture 1: Data Detail n Data Sanity check

Lecture 2: Prepare Data for clustering

Lecture 3: Variable selection by Variable clutsering

Lecture 4: decide final number of clusters

Lecture 5: Iterate for final cluster

Lecture 6: Investigate the clusters

Lecture 7: Business Presentation of cluster solution

Lecture 8: Concluding Tips

Chapter 10: Part 04 – Practice Assignment and model solution

Lecture 1: Practice

Lecture 2: Model solution using R

Lecture 3: Model Solution using SAS

Lecture 4: FAQ (will keep growing overtime based on student's queries)

Lecture 5: Bonus Topic – Analytics / Data Science / Machine Learning Interview questions