Course Description: Data mining is used to discover patterns and relationships in data. Emphasis is on large complex data sets such as those in very large databases or through web mining. Topics: decision trees, neural networks, association rules, clustering, case based methods, and data visualization. The following chapters from the textbook will be covered in this order:

  • Chapter 1 – Introduction
  • Chapter 2 – Data
  • Chapter 3 – Exploring Data
  • Chapter 6 – Association Analysis: Basic Concepts and Algorithms
  • Chapter 4 – Classification: Basic Concepts, Decision Trees, and Model Evaluation
  • Chapter 5 – Classification: Alternative Techniques (naive bayes models, support vector machines)
  • Chapter 8 – Cluster Analysis: Basic Concepts and Algorithms
  • Chapter 10 – Anomaly Detection

Course Schedule and Lecture Notes in PDF format from Stanford for Statistical Aspects of Data Mining.

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