Cluster Analysis- Theory & workout using SAS and R
- 3.7
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5 hours on-demand video
$
12.99
Brief Introduction
Unsupervised Machine Learning : Hierarchical & non hierarchical clustering (k-means), theory & SAS / R programDescription
- 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
Requirements
- Requirements
- Basic understanding of statistics
- Basic knowledge of SAS
- Basic knowledge of R
Gopal Prasad Malakar
3.7 Raiting
Cluster Analysis- Theory & workout using SAS and R
- 3.7
-
![]()
![]()
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![]()
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Unsupervised Machine Learning : Hierarchical & non hierarchical clustering (k-means), theory & SAS / R program At the same time, the groups are as dissimilar to other groups as possible....
