Association mining in time-varying domains

Antonin Rozsypal, Miroslav Kubat

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

The input of a classical application of association mining is a large set of transactions, each consisting of a list of items a customer has paid for at a supermarket checkout desk. The goal is to identify groups of items (itemsets) that frequently co-occur in the same shopping carts. This paper focuses on an aspect that has so far received relatively little attention: the composition of the list of frequent itemsets may change in time as the purchasing habits get affected by fashion, season, and introduction of new products. We investigate (1) heuristics for the detection of such changes in time-ordered databases and (2) techniques that update the set of frequent itemsets when the change is detected. As the main performance criterion, we use the accuracy with which our program maintains the current list of frequent itemsets in a time-varying environment.

Original languageEnglish (US)
Pages (from-to)273-288
Number of pages16
JournalIntelligent Data Analysis
Volume9
Issue number3
DOIs
StatePublished - 2005

Keywords

  • association mining
  • change detection
  • similarity metrics

ASJC Scopus subject areas

  • Artificial Intelligence
  • Theoretical Computer Science
  • Computer Vision and Pattern Recognition

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