Cluster Analysis

Cluster Analysis

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.

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.

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Overview

In marketing disciplines, cluster analysis is the basis for identifying clusters of customer records, a process call market segmentation. This is a hands-on course in which you will use statistical software to apply cluster method algorithms to real data, and interpret the results. You will study the most widely used models including: hierarchical clustering (in which smaller clusters are nested inside larger clusters): k-means clustering; two-step clustering; and normal mixture models for continuous variables.

 

Learning Outcomes

After taking this course students will have insights into various cluster analysis methods. They will be able to identify clusters in multivariate data, apply normalization techniques, and correctly interpret the output of different clustering procedures.

  • Conduct hierarchical cluster analysis and k-means clustering to identify clusters in multivariate data 
  • Hierarchical clustering (in which smaller clusters are nested inside larger clusters)
  • K-means and Two-step clustering
  • Normal mixture models for continuous variables
  • Interpret/diagnose the output of different clustering procedures
  • 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

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).

Instructors

dr-anthony-babinec

Mr. Anthony Babinec

Anthony Babinec is the President of AB Analytics. For over two decades, Tony Babinec has specialized in the application of statistical and data mining methods to the solution of business problems. Before forming AB Analytics, Babinec was Director of Advanced Products Marketing at SPSS; he worked on the marketing of Clementine and introduced CHAID, neural nets and other advanced technologies to SPSS users. He is on the Board of Directors of the Chicago Chapter of the American Statistical Association, where he has held various offices including President.
See Instructor Bio

Course Syllabus

Week 1

Hierarchical Clustering

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

Week 2

K-means Clustering

  • K-means Clustering

Week 3

Normal Mixture Model

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

Week 4

Other Approaches

  • Other approaches will be discussed

Class Dates

2019

No classes scheduled at this time.

2020

May 29, 2020 to May 29, 2020

2021

No classes scheduled at this time.

Send me reminder for next class

Prerequisites

There are no prerequisites for this course.

Some familiarity with multivariate data is helpful, such as that provided in the following courses, although the specific methods discussed in these courses are not required for this course:

Predictive Analytics 1 – Machine Learning Tools

This course introduces to the basic concepts in predictive analytics to visualize and explore data that account for most business applications of predictive modeling: classification and prediction.
Topic: Analytics, Prediction/Forecasting | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Jan 10, 2020, May 15, 2020, Sep 11, 2020, Jan 15, 2021

Regression Analysis

This course will teach you how multiple linear regression models are derived, assumptions in the models, how to test whether data meets assumptions, and develop strategies for building and understanding useful models.
Topic: Statistics, Statistical Modeling | Skill: Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Jan 17, 2020, May 8, 2020, Oct 2, 2020

What Our Students Say​

I was quite surprised and impressed with the depth of theory development in this area, plus the software options to allow optimum exploration of the data. Thanks for a most interesting course.

Daivd Brown

Professor Babinec did a wonderful job of leading us through this material.  It is obvious that he has a passion for this subject.  His breadth of knowledge of and experience with cluster analysis added significantly to the course.  He gave very helpful answers in the discussion forum!  Anuja, our teaching assistant, was very supportive throughout the course as well.  The course material was challenging but fulfilling, helping us appreciate the subtleties of cluster analysis rather than thoughtlessly plunge ahead.  In summary, this was a very satisfying and useful course.

William Barbour
Audi of America through Modis

Frequently Asked Questions

Can I transfer or withdraw from a course?

We have a flexible transfer and withdrawal policy that recognizes circumstances may arise to prevent you from taking a course as planned. You may transfer or withdraw from a course under certain conditions.

  • Students are entitled to a full refund if a course they are registered for is canceled.
  • You can transfer your tuition to another course at any time prior to the course start date or the drop date, however a transfer is not permitted after the drop date.
  • Withdrawals on or after the first day of class are entitled to a percentage refund of tuition.

Please see this page for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

Do your courses have for-credit options?

Our courses have several for-credit options:

  • Continuing education units (CEU)
  • College credit through The American Council on Education (ACE CREDIT)
  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

Please see our knowledge center for more information.

Is the Institute for Statistics Education certified?

The Institute for Statistics Education is certified to operate by the State Council of Higher Education for Virginia (SCHEV). For more information visit: http://www.schev.edu

Please see our knowledge center for more information.

Visit our knowledge base and learn more.

FAQs + Knowledge Base

Related Courses

Predictive Analytics 1 – Machine Learning Tools

This course introduces to the basic concepts in predictive analytics to visualize and explore data that account for most business applications of predictive modeling: classification and prediction.
Topic: Analytics, Prediction/Forecasting | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Jan 10, 2020, May 15, 2020, Sep 11, 2020, Jan 15, 2021

Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules

This course will teach you key unsupervised learning techniques of association rules – principal components analysis, and clustering – and will include an integration of supervised and unsupervised learning techniques.
Topic: Analytics, Prediction/Forecasting | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Jan 3, 2020, Apr 10, 2020, Jul 31, 2020

Additional Course Information

Organization of Course

This course takes place online 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.

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.

Time Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.

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.

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 a copy of your course textbook prior to course start 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.  

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

Course Fee & Information

Enrollment
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to Statistics.com?  Click here for a special introductory discount code.  

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

INFORMS-CAP
This course is recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam and can help CAP® analysts accrue Professional Development Units to maintain their certification.

ACE CREDIT | College Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for the the upper division baccalaureate degree, 3 semester hours in data mining, statistics, or computer science. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.

ACE Digital Badge
Courses evaluated by the American Council on Education (ACE) have a digital badge available for successful completion of the course.

Supplemental Information

There is no supplemental content for this course.

Miscellaneous

There is no additional information for this course.

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Cluster Analysis
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