Cluster Analysis

taught by Tony Babinec

This course will teach you how to use various cluster analysis methods to identify possible clusters in multivariate data. Methods discussed include hierarchical clustering, k-means clustering, two-step clustering, and normal mixture models for continuous variables.

• 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).

Cluster Analysis

taught by Tony Babinec

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Cluster Analysis

taught by Tony Babinec

This course will teach 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.

Some familiarity with multivariate data is also helpful, such as that provided in Introduction to Predictive Modeling (though the methods discussed in that course are not required for this course).

SESSION 1: Hierarchical Clustering

• Hierarchical clustering - dendrograms
• Divisive vs. agglomerative methods
• Different linkage methods

SESSION 2: K-means Clustering

SESSION 3: Normal Mixture Model

• Finite mixture model
• K-means cluster as a special case

SESSION 4: Other Approaches

HOMEWORK:

Homework in this course consists of short answer questions to test concepts and guided data analysis problems using software.

• SPSS two-step
• COSA - Clustering Objects on Subsets of Attributes (runs under R)

This course takes place over the internet at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

The course typically requires 15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
2. You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
3. You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course, 5.0 CEU's and a record of course completion will be issued by The Institute, upon request.

The required text for this course is Cluster Analysis, 5th Edition, by Brian S. Everitt, Dr Sabine Landau, Dr Morven Leese, Dr Daniel Stahl, and it can be ordered from Wiley by clicking here. Wiley typically offers statistics.com customers up to 15% discount on this book (and all other statistics titles): enter the code aff15 in the Promotion Code field when prompted during checkout and click the Apply Discount button. (If you are located in Asia, the web procedure for your location may not accept this discount – try calling your regional Wiley representative.).

PLEASE ORDER YOUR COPY IN TIME FOR THE COURSE STARTING DATE.

Some illustrations will be provided using XLMiner (an Excel add-in) and SPSS.   Instructor is familiar with both, but TA will be able to provide assistance only with XLMiner. Click Here for information on obtaining a free (or nominal cost) copy of XLMiner and SPSS.

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