Mining inter-transactional association rules: Generalization and empirical evaluation

Ling Feng, Qing Li, Allan Wong

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)


The problem of mining multidimensional inter-transactional association rules was recently introduced in [5, 4]. It extends the scope of mining association rules from traditional single-dimensional intra- transactional associations to multidimensional inter-transactional associations. Inter-transactional association rules can represent not only the associations of items happening within transactions as traditional intra- transactional association rules do, but also the associations of items among different transactions under a multidimensional context. “After McDonald and Burger King open branches, KFC will open a branch two months later and one mile away” is an example of such rules. In this paper, we extend the previous problem definition based on context expansions, and present a generalized multidimensional inter-transactional association rule framework. An algorithm for mining such generalized inter-transactional association rules is presented by extension of Apriori. We report our experiments on applying the algorithm to real-life data sets. Empirical evaluation shows that with the generalized inter- transactional association rules, more comprehensive and interesting association relationships can be detected.

Original languageEnglish
Title of host publicationData Warehousing and Knowledge Discovery - 3rd International Conference, DaWaK 2001, Proceedings
EditorsWerner Winiwarter, Yahiko Kambayashi, Masatoshi Arikawa
Number of pages10
ISBN (Print)3540425535, 9783540425533
Publication statusPublished - 1 Jan 2001
Externally publishedYes
Event3rd International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2001 - Munich, Germany
Duration: 5 Sept 20017 Sept 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2001

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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