7. Data Mining Algorithms II: Frequent Item Sets

  1. Amiya Nayak B.Math., Ph.D. Adjunct Research Professor Associate Editor Full Professor2 and
  2. Ivan Stojmenović Ph.D. Chair Professor founder editor-in-chief2,3
  1. Dan A. Simovici

Published Online: 1 MAR 2007

DOI: 10.1002/9780470175668.ch7

Handbook of Applied Algorithms: Solving Scientific, Engineering and Practical Problems

Handbook of Applied Algorithms: Solving Scientific, Engineering and Practical Problems

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

  1. 2

    SITE, University of Ottawa, 800 King Edward Ave., Ottawa, ON K1N 6N5, Canada

  2. 3

    EECE, University of Birmingham, UK

Author Information

  1. Department of Mathematics and Computer Science, University of Massachusetts at Boston, Boston, MA 02125, USA

Publication History

  1. Published Online: 1 MAR 2007
  2. Published Print: 14 FEB 2008

ISBN Information

Print ISBN: 9780470044926

Online ISBN: 9780470175668

SEARCH

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.