Feasible Itemset Distributions in Data Mining: Theory and Application

Ganesh Ramesh, William A. Maniatty, Mohammed J. Zaki

Research output: Contribution to conferencePaper

29 Citations (Scopus)

Abstract

Computing frequent itemsets and maximally frequent itemsets in a database are classic problems in data mining. The resource requirements of all extant algorithms for both problems depend on the distribution of frequent patterns, a topic that has not been formally investigated. In this paper, we study properties of length distributions of frequent and maximal frequent itemset collections and provide novel solutions for computing tight lower bounds for feasible distributions. We show how these bounding distributions can help in generating realistic synthetic datasets, which can be used for algorithm benchmarking.

Original languageEnglish
Pages284-295
Number of pages12
Publication statusPublished - 1 Dec 2003
EventTwenty second ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2003 - San Diego, CA, United States
Duration: 9 Jun 200311 Jun 2003

Other

OtherTwenty second ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2003
CountryUnited States
CitySan Diego, CA
Period9/6/0311/6/03

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ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture

Cite this

Ramesh, G., Maniatty, W. A., & Zaki, M. J. (2003). Feasible Itemset Distributions in Data Mining: Theory and Application. 284-295. Paper presented at Twenty second ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2003, San Diego, CA, United States.