Cluster Analysis

Cluster Analysis

taught by Anthony Babinec

 

 
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Aim of Course:

In this online course, “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
This course may be taken individually (one-off) or as part of a certificate program.
Course Program:

WEEK 1: Hierarchical Clustering

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


WEEK 2: K-means Clustering


WEEK 3: Normal Mixture Model

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


WEEK 4: Other Approaches


HOMEWORK:

Homework in this course consists of short answer questions to test concepts and guided data analysis problems using software. In addition to assigned readings, this course also has an end of course data modeling project.

Cluster Analysis

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).
Level:
Intermediate
Prerequisite:
Some familiarity with multivariate data is also helpful, such as that provided in Regression or Predictive Analytics 1 (though the specific methods discussed in those courses are not required for this course).
Organization of the Course:
Options for Credit and Recognition:
Course Text:

This course will use papers that will be made available electronically, and will also refer to sections from the book Cluster Analysis, 5th Edition, by Brian S. Everitt, Dr Sabine Landau, Dr Morven Leese, Dr Daniel Stahl.

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

Software:
This is a hands-on course. Participants will apply cluster methods algorithms to real data, and interpret the results, so software capable of doing cluster analysis is required. The model solutions for the assignments were developed in IBM SPSS Statistics and Latent Gold. In addition, we also provide solutions using R. Other possible choices include XLStat and Analytic Solver Data Mining.  For information on software, including free licenses for students, click here.
Instructor(s):

Dates:

May 31, 2019 to June 28, 2019 May 29, 2020 to June 26, 2020

Cluster Analysis

Instructor(s):

Dates:
May 31, 2019 to June 28, 2019 May 29, 2020 to June 26, 2020

Course Fee: $589

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Click here to get information on group rates. 

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

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