数据挖掘课件数据挖掘05.pdf

Data Mining:
Concepts and Techniques
— Chapter 5 —
Jiawei Han
Department puter Science
University of Illinois at Urbana-Champaign
/~hanj
©2006 Jiawei Han and Micheline Kamber, All rights reserved
2011-1-19 School of Management, HUST 1
Chapter 5: Mining Frequent Patterns,
Association and Correlations
 Basic concepts and a road map
 Efficient and scalable frequent itemset mining
methods
 Mining various kinds of association rules
 From association mining to correlation
analysis
 Constraint-based association mining
 Summary
2011-1-19 School of Management, HUST 2
Chapter 5: Mining Frequent Patterns,
Association and Correlations
 Basic concepts and a road map
 Efficient and scalable frequent itemset mining
methods
 Mining various kinds of association rules
 From association mining to correlation
analysis
 Constraint-based association mining
 Summary
2011-1-19 School of Management, HUST 3
What Is Frequent Pattern Analysis?
 Frequent pattern: a pattern (a set of items, subsequences, substructures,
etc.) that occurs frequently in a data set
 First proposed by Agrawal, Imielinski, and Swami [AIS93] in the context
of frequent itemsets and association rule mining
 Motivation: Finding inherent regularities in data
 What products were often purchased together?— Beer and diapers?!
 What are the subsequent purchases after buying a PC?
 What kinds of DNA are sensitive to this new drug?
 Can we automatically classify web documents?
 Applications
 Basket data analysis, cross-marketing, catalog design, sale campaign
analysis, Web log (click stream) analysis, and DNA sequence analysis.
2011-1-19 School of Management, HUST 4
Why Is Freq. Pattern Mining Important?
 Discloses an intrinsic and important property of data sets
 Forms the foundation for many essential data mining tasks
 Association, correlation, and causality analysis
 Sequential, structural (., sub-