Cluster Analysis,” you will you how to use various cluster analysis methods to identify possible clusters in multivariate data. In marketing applications, clusters of customer records are called market segments (and the process is called market segmentation). Methods discussed include:
- Hierarchical clustering (in which smaller clusters are nested inside larger clusters);
- k-means clustering;
- two-step clustering;
- Normal mixture models for continuous variables.
After taking this course, a student will be able to:
- Conduct hierarchical cluster analysis and k-means clustering to identify clusters in multivariate data
- Apply normalization of data appropriately in cluster analysis
- Identify the assignment of cases to clusters
- Apply mixture models to multivariate data and interpret the output
- Interpret/diagnose the output of different clustering procedures
- Week 1: Hierarchical Clustering
- Week 2: K-means Clustering
- Week 3: Normal Mixture Model
- Week 4: Other Approaches
June 03, 2016 to July 01, 2016
Duration: 4 Weeks
About 15 hours per week, at times of your choosing.
Fees: INR 37,740 (assuming $ = INR 60)
Part Time/Full Time:
Who Should Take This Course:
- Marketing analysts who need to cluster customer data as part of a market segmentation strategy;
- Computational biologists (e.g. for taxonomy);
- Environmental scientists (e.g. for habitat studies);
- IT specialists (e.g. in modeling web traffic patterns);
- Military and national security analysts (e.g. in automated analysis of intercepted communications).
- Some familiarity with multivariate data is helpful.
- Anthony Babinec