数据挖掘中的模式发现

Module 1 consists of two lessons. Lesson 1 covers the general concepts of pattern discovery. This includes the basic concepts of frequent patterns, closed patterns, max-patterns, and association rules. Lesson 2 covers three major approaches for mining frequent patterns. We will learn the downward closure (or Apriori) property of frequent patterns and three major categories of methods for mining frequent patterns: the Apriori algorithm, the method that explores vertical data format, and the pattern-growth approach. We will also discuss how to directly mine the set of closed patterns.

Module 2 covers two lessons: Lessons 3 and 4. In Lesson 3, we discuss pattern evaluation and learn what kind of interesting measures should be used in pattern analysis. We show that the support-confidence framework is inadequate for pattern evaluation, and even the popularly used lift and chi-square measures may not be good under certain situations. We introduce the concept of null-invariance and introduce a new null-invariant measure for pattern evaluation. In Lesson 4, we examine the issues on mining a diverse spectrum of patterns. We learn the concepts of and mining methods for multiple-level associations, multi-dimensional associations, quantitative associations, negative correlations, compressed patterns, and redundancy-aware patterns.

Module 3 consists of two lessons: Lessons 5 and 6. In Lesson 5, we discuss mining sequential patterns. We will learn several popular and efficient sequential pattern mining methods, including an Apriori-based sequential pattern mining method, GSP; a vertical data format-based sequential pattern method, SPADE; and a pattern-growth-based sequential pattern mining method, PrefixSpan. We will also learn how to directly mine closed sequential patterns. In Lesson 6, we will study concepts and methods for mining spatiotemporal and trajectory patterns as one kind of pattern mining applications. We will introduce a few popular kinds of patterns and their mining methods, including mining spatial associations, mining spatial colocation patterns, mining and aggregating patterns over multiple trajectories, mining semantics-rich movement patterns, and mining periodic movement patterns.