Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
4 Hour(s) 59 Minute(s)
Language
English
Taught by
Gopal Prasad Malakar
Rating
Course Overview
Unsupervised Machine Learning : Hierarchical & non hierarchical clustering (k-means), theory & SAS / R program
- 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.
- Course materials- The course contains video presentations (power point presentations with voice), pdf, excel work book and sas codes.
- Course duration- The course should take roughly 10 hours to understand and internalize the concepts.
- Course Structure (contents) The structure of the course is as follows.
Part 01 - cluster analysis theory and workout using SAS
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Motivation –
- 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
Part 02
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Learn R syntax for hierarchical and non hierarchical clustering
Part 03
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Cluster analysis in data mining scenario
Part 04
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Assignment on cluster analysis
Course Content
- 10 section(s)
- 64 lecture(s)
- Section 1 Overall structure of the course
- Section 2 Part 01 - Cluster Analysis using SAS
- Section 3 Motivation, Industry Applications & clustering as strategy. Industry Case study
- Section 4 Hierarchical Clustering
- Section 5 Non Hierarchical clustering - K means clustering
- Section 6 Variants of Hierarchical clustering, Different distance and linkage functions
- Section 7 Part 02- cluster Analysis using R
- Section 8 Part 03 - Cluster Analysis in data mining scenario (industrial set up)
- Section 9 Demo of clustering approach for data mining scenario using R
- Section 10 Part 04 - Practice Assignment and model solution
What You’ll Learn
- 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, R syntax for clustering
Skills covered in this course
Reviews
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HHONG HENG WAH
Good match but concepts needs to be pictorially supported and better sequenced
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MMihai Adrian
The teacher does a relatively good job by explaining the SAS and R clustering output . Sometimes, however, the explanation can be a bit confusing. Moreover, it does not explain clustering methods that accommodate categorical variables. I do recommend this course not as a full fledged course but as a starting point.
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MMaurizio Niccoli
difficult to understand the instructor - sort the accent out
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RRajesh
Course instructor explains the concepts very clearly and takes you gradually to the next level. I will recommend his approach. Good teacher knowing what he teaches and makes the students to understand.
