Evaluation of sampling for data mining of association rules

Mohammed Javeed Zaki, Srinivasan Parthasarathy, Wei Li, Mitsunori Ogihara

Research output: Contribution to conferencePaperpeer-review

103 Scopus citations

Abstract

Discovery of association rules is a prototypical problem in data mining. The current algorithms proposed for data mining of association rules make repeated passes over the database to determine the commonly occurring itemsets (or set of items). For large databases, the I/O overhead in scanning the database can be extremely high. In this paper we show that random sampling of transactions in the database is an effective method for finding association rules. Sampling can speed up the mining process by more than an order of magnitude by reducing I/O costs and drastically shrinking the number of transactions to be considered. We may also be able to make the sampled database resident in main-memory. Furthermore, we show that sampling can accurately represent the data patterns in the database with high confidence. We experimentally evaluate the effectiveness of sampling on different databases, and study the relationship between the performance, and the accuracy and confidence of the chosen sample.

Original languageEnglish (US)
Pages42-50
Number of pages9
StatePublished - Jan 1 1997
Externally publishedYes
EventProceedings of the 1997 7th International Workshop on Research Issues in Data Engineering, RIDE'97 - Birmingham, UK
Duration: Apr 7 1997Apr 8 1997

Other

OtherProceedings of the 1997 7th International Workshop on Research Issues in Data Engineering, RIDE'97
CityBirmingham, UK
Period4/7/974/8/97

ASJC Scopus subject areas

  • Software
  • Engineering (miscellaneous)
  • Hardware and Architecture

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