Chapter 7. Data Mining Algorithms II: Frequent Item Sets
- Amiya Nayak B.Math., Ph.D. Adjunct Research Professor Associate Editor Full Professor2,
- Ivan Stojmenović Ph.D. Chair Professor founder editor-in-chief2,3
Published Online: 1 MAR 2007
DOI: 10.1002/9780470175668.ch7
Copyright © 2008 John Wiley & Sons, Inc.
Book Title
Handbook of Applied Algorithms: Solving Scientific, Engineering and Practical Problems
Additional Information
How to Cite
Simovici, D. A. (2007) Data Mining Algorithms II: Frequent Item Sets, in Handbook of Applied Algorithms: Solving Scientific, Engineering and Practical Problems (eds A. Nayak and I. Stojmenović), John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9780470175668.ch7
Editor Information
- 2
SITE, University of Ottawa, 800 King Edward Ave., Ottawa, ON K1N 6N5, Canada
- 3
EECE, University of Birmingham, UK
Publication History
- Published Online: 1 MAR 2007
- Published Print: 14 FEB 2008
ISBN Information
Print ISBN: 9780470044926
Online ISBN: 9780470175668
- Summary
- Chapter
Keywords:
- data mining algorithms II - frequent item sets;
- association rules;
- Apriori algorithm and frequent item set identification
Summary
The identification of frequent item sets and of association rules have received a lot of attention in data mining due to their many applications in marketing, advertising, inventory control, and many other areas. First the notion of frequent item set is introduced and we study in detail the most popular algorithm for item set identification: the Apriori algorithm. Next we present the role of frequent item sets in the identification of association rules and examine the levelwise algorithms, an important generalization of the Apriori algorithm.