Course Description
Clustering and Association Rule Mining are two of the most frequently used Data Mining technique for various functional needs, especially in Marketing, Merchandising, and Campaign efforts.
Clustering helps find natural and inherent structures amongst the objects, where as Association Rule is a very powerful way to identify interesting relations between objects in large commercial databases. The main motivation for the course is:
i) This course specifically touches upon the scenarios where Clustering is necessary, and which Clustering technique is appropriate for which scenario.
ii) This course also stresses on advantages as well as practical issues with different Clustering techniques
What am I going to get from this course?
- Learn clustering through examples in R – that you immediately apply in your day-to-day work
- Over 20 lectures and 5-6 hours of content, plus 2 practice exercises on Clustering and Market Basket Analysis
- Learn practical Hierarchical, Non-Hierarchical, Density based clustering techniques. Also Association rules and Market Basket Analysis
Curriculum
Module 1: Overview of Unsupervised Learning
53:23
Lecture 1
Introduction to Unsupervised Learning
11:49
Review the basics of Unsupervised Learning
Lecture 2
Exploratory Data Analysis
11:24
Understand Exploratory Data Analysis (EDA) with R sessions
Lecture 3
R Session on Exploratory Data Analysis (Part 1)
12:21
Practical R-session on Correlation
Lecture 4
R Session on Exploratory Data Analysis (Part 2)
11:54
Practical R-session on Principal Components
Lecture 5
Introduction to Clustering
05:55
Get started to understand the Clustering basics
Module 2: K-Means Clustering
01:02:10
Lecture 6
Introductory Basics
17:33
Understand different mathematical basics and terminologies needed for K-Means
Lecture 7
K-Means Algorithm
08:24
Lecture 8
How to decide K?
08:48
Lecture 9
Example through R
23:27
Lecture 10
Further Discussions
03:58
Module 3: Hierarchical Clustering
50:31
Lecture 11
Introduction
11:04
Understand extended distance measures, dendrogram etc.
Lecture 12
Quick Algorithm
07:53
Lecture 13
Cluster number determination
08:01
Lecture 14
Example through R
18:35
Lecture 15
Further Discussions
04:58
Module 4: Density based Clustering
54:56
Lecture 16
Introduction & Terminologies
16:13
Lecture 17
DBSCAN Algorithm
06:05
Lecture 18
Choice of parameters
13:24
How do we empirically choose optimal parameters?
Lecture 19
Example through R
15:29
Lecture 20
Further Discussions
03:45
Module 5: Association Rules (AR)
01:02:36
Lecture 21
Introduction
13:05
Lecture 22
The Apriori Nature
15:34
Lecture 23
Market Basket Analysis
10:16
Lecture 24
Example through R
20:00
Lecture 25
Further Discussions
03:41
Lecture 26
Practice Exercises on Clustering & Association Rule
2 practice exercises, each on Clustering and Association Rule Mining
