Book Resources

This page contains online book resources for instructors and students. You can contact us via email if you have any questions.

Book Figures


Part I: Exploratory Data Analysis

  • Chapter 1: Data Mining and Analysis: PDF, PPT
  • Chapter 2: Numeric Attributes: PDF, PPT
  • Chapter 3: Categorical Attributes: PDF, PPT
  • Chapter 4: Graph Data: PDF, PPT
  • Chapter 5: Kernel Methods: PDF, PPT
  • Chapter 6: High-dimensional Data: PDF, PPT
  • Chapter 7: Dimensionality Reduction: PDF, PPT

Part II: Frequent Pattern Mining

  • Chapter 8: Itemset Mining: PDF, PPT
  • Chapter 9: Summarizing Itemsets: PDF, PPT
  • Chapter 10: Sequence Mining: PDF, PPT
  • Chapter 11: Graph Pattern Mining: PDF, PPT
  • Chapter 12: Pattern and Rule Assessment: PDF, PPT

Part III: Clustering

  • Chapter 13: Representative-based Clustering: PDF,PPT
  • Chapter 14: Hierarchical Clustering: PDF, PPT
  • Chapter 15: Density-based Clustering: PDF, PPT
  • Chapter 16: Spectral and Graph Clustering: PDF, PPT
  • Chapter 17: Clustering Validation: PDF, PPT

Part IV: Classification

  • Chapter 18: Probabilistic Classification: PDF, PPT
  • Chapter 19: Decision Tree Classifier: PDF, PPT
  • Chapter 20: Linear Discriminant Analysis: PDF, PPT
  • Chapter 21: Support Vector Machines: PDF, PPT
  • Chapter 22: Classification Assessment: PDF, PPT

Latex Sources for Slides

  • Complete latex sources for all slides GitHub (latest). These are also available in ZIP format.

Book Datasets

All the datasets used in the different chapters in the book as a zip file. Read the readme.txt in the directory:

Lecture Videos

You can access the lecture videos for the data mining course offered at RPI in Fall 2009.

Implementation-based Projects

Here are some implementation-based project ideas. You can use python or R, or any other language/software of your choice.

Solutions Manual

Solutions are available for Instructors. Please email us from an official academic email address to request the solutions manual.