Mining frequent itemsets using support constraints

Ke Wang, Yu He, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Interesting patterns often occur at varied levels of support. The classic association mining based on a uniform minimum support, such as Aprio.-i, either misses interesting patterns of low support or suffers from the bottleneck of itemset generation. A better solution is to exploit Support constraints, which specify what minimum support is required for what itemseta, so that only necessary itemsets are generated. In this paper, we present a framework of frequent itemset mining in the presence of support constraints. Our approach is to "push" support constraints into the Apriori it.emset generation so that the "best" minimum support is used for each itemset at run time to preserve the essence of Apriori.

Original languageEnglish (US)
Title of host publicationProceedings of the 26th International Conference on Very Large Data Bases, VLDB'00
Pages43-52
Number of pages10
StatePublished - 2000
Externally publishedYes
Event26th International Conference on Very Large Data Bases, VLDB 2000 - Cairo, Egypt
Duration: Sep 10 2000Sep 14 2000

Publication series

NameProceedings of the 26th International Conference on Very Large Data Bases, VLDB'00

Other

Other26th International Conference on Very Large Data Bases, VLDB 2000
Country/TerritoryEgypt
CityCairo
Period9/10/009/14/00

ASJC Scopus subject areas

  • Hardware and Architecture
  • Information Systems
  • Software
  • Information Systems and Management

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