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

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

This course, with a focus on R, 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.


Complex sample designs such as stratified cluster sampling make it possible to extract maximum information at minimum cost, but they are typically harder to work with than simple random samples. How do you analyze the resulting data – in particular, how do you determine margins of error? This course teaches you how to estimate variances when analyzing survey data from complex samples, and also how to fit linear and logistic regression models to complex sample survey data.

  • Introductory, Intermediate
  • 4 Weeks
  • Expert Instructor
  • Tuiton-Back Guarantee
  • 100% Online
  • TA Support

Learning Outcomes

After completing this course students will understand issues relating to using too many predictors and how to reduce the number of predictors to a smaller number of usable “components.” You will use various clustering techniques and association rules to describe clusters of similar records, and to find patterns in your data. You will learn to use R to implement the models covered in this course, and how to combine supervised and unsupervised models.

  • Use principal coponents analysis and variable selection techniques to reduce dimensionality
  • Cluster records using hierarchical and k-means clustering
  • Discover association rules in transaction databases
  • Specify how collaborative filtering can be used to develop automated recommendations
  • Integrate unsupervised and supervised data mining methods in a case study
  • Use various R packages to implement the models in the course

Who Should Take This Course

Marketers seeking to specify customer segments, identify associations among products purchased and design recommender systems, 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.

Our Instructors

Course Syllabus

Week 1

Dimension Reduction

  • Detecting information overlap using domain knowledge and data summaries and charts
  • Removing or combining redundant variables and categories
  • Dealing with multi-category variables
  • Automated dimension reduction techniques
    • Principal Components Analysis (PCA)
    • Predictive algorithms with variable selection techniques

Week 2

Cluster Analysis

  • Popular uses of cluster analysis
  • Clustering approaches
  • Hierarchical clustering
    • Distances between records
    • Distances between clusters
    • Dendrograms
    • Validating clusters
    • Strengths and weaknesses
  • K-Means Clustering
    • Initializing the k clusters
    • Distance of a record from a cluster
    • Within-cluster homogeneity
    • Elbow charts

Week 3

Association Rules and Recommender Systems

  • Discovering association rules in transaction databases
    • Support, confidence and lift
    • The apriori algorithm
    • Shortcomings
  • Collaborative filtering
    • Person-based
    • Item-based

Week 4

Integrating Supervised and Unsupervised Methods; Introduction to Network and Text Analytics

  • The role of unsupervised methods in predictive analytics
    • Dimension reduction of predictor space
    • Predictive models on subsets of homogeneous records
  • Advantages and weaknesses of combining unsupervised and supervised methods
  • Network analytics
  • Text analytics
  • Unsupervised methods used in network and text analytics


In addition, there is a lesson in the course where supervised and unsupervised learning techniques are used in combination, so, unless you do not need this portion, you should be familiar with supervised learning methods, such as those presented in Predictive Analytics 1 with R.

Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

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

Additional Information

Time Requirements

About 15 hours per week, at times of your choosing.


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 supplemental video lectures and an end of course data modeling project.

Course Text

The recommended text for this course is Data Mining for Business Analytics: Concepts, Techniques, and Applications in R, by Shmueli, Patel, Yahav, Bruce and Lichtendahl. This same text is also used in the previous courses: “Predictive Analytics 1 – Machine Learning Tools – with R” and “Predictive Analytics 2 – Neural Nets and Regression – with R”.


This is a hands-on course, and participants will apply data mining algorithms to real data. The course will use R, a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.

Supplemental Information

Literacy, Accessibility, and Dyslexia

At, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:







  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)

Register For This Course

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