Friday, 28 March 2014

Data Mining: Unsupervised Techniques

Data mining, the art and science of learning from data, covers a number of different procedures. This course covers key unsupervised learning techniques: association rules, principal components analysis, and clustering. (Introduction to Predictive Modeling covers techniques that are used to predict a record's class or the value of an outcome variable on the basis of a set of records with known outcomes).

Learn with Mr. Anthony Babinec in online course " Data Mining: Unsupervised Techniques" at For more details please visit at
We, C-eLT, Pune, partner with and offer these courses to Indian participants at special prices payable in INR.

The course will include an integration of supervised and unsupervised learning techniques. This is a hands-on course - participants in the course will have access to an Excel-based comprehensive tool for data-mining, XLMiner, the use of which will be explained in the course. Participants will apply data mining algorithms to real data, and will interpret the results.

Who Should Take This Course:
Marketers seeking to specify customer segments and identify associations among products purchased, environment scientists seeking to cluster observations, analysts who need to identify the key variables out of many, MBA's seeking to update their knowledge of quantitative techniques, managers and scientists who want to see what data-mining can do, and anyone who wants a practical hands-on grounding in basic data-mining techniques.

Course Program:

Course outline: The course is structured as follows
SESSION 1: Principal Components Analysis
  • The goal - dimensionality reduction
  • The principal components
  • Scale variance estimation
  • Normalizing the data
  • Principal components and least orthogonal squares
  • Exercises
 SESSION 2: Clustering
  • What is cluster analysis?
  • Hierarchical methods
  • Nearest neighbor (single linkage)
  • Farthest neighbor (complete linkage)
  • Group average (average linkage)
  • Optimization and the k-means algorithm
  • Similarity measures
  • Other distance measures
  • The curse of dimensionality
  • Exercises
SESSION 3: Association Rules
  • Discovering association rules in transaction databases
  • Support and confidence
  • The apriori algorithm
  • Shortcomings
  • Exercises
SESSION 4: Integration of Supervised and Unsupervised learning
  • Clustering into customer segments
  • Profiling of customer segments
  • Classifying new records by segment
The final lesson is an integration of supervised and unsupervised techniques. To get the full benefit of this course, familiarity with supervised learning is needed, but those not requiring this integration can learn about clustering, association rules and principal components without having had a course in supervised learning.

The instructor, Anthony Babinec, is the president of AB Analytics and served previously as 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.

You will be able to ask questions and exchange comments with the instructors via a private discussion board throughout the course.   The courses take place online at in a series of 4 weekly lessons and assignments, and require about 15 hours/week.  Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.

We, the Center for eLearning and Training (C-eLT), Pune, partner with and offer these courses to Indian participants at special prices payable in INR.

For India Registration and pricing, please visit us at

Call: 020 6680 0300 / 322


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