Skip to main content
Article thumbnail
Location of Repository

Evaluation of sampling for data mining of association rules

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


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 transaction to be considered. We may also be able to makethe 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. 1

Year: 1996
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • (external link)
  • (external link)
  • Suggested articles

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.