Mining inter-transactional association rules : Generalization and empirical evaluation

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationData Warehousing and Knowledge Discovery
Subtitle of host publication3rd International Conference, DaWaK 2001, Proceedings
EditorsWerner Winiwarter, Yahiko Kambayashi, Masatoshi Arikawa
PublisherSpringer Verlag
Pages31-40
Volume2114
ISBN (Print)3540425535, 9783540425533
Publication statusPublished - 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2114
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Title3rd International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2001
PlaceGermany
CityMunich
Period5 - 7 September 2001

Abstract

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.

Citation Format(s)

Mining inter-transactional association rules : Generalization and empirical evaluation. / Feng, Ling; Li, Qing; Wong, Allan.

Data Warehousing and Knowledge Discovery: 3rd International Conference, DaWaK 2001, Proceedings. ed. / Werner Winiwarter; Yahiko Kambayashi; Masatoshi Arikawa. Vol. 2114 Springer Verlag, 2001. p. 31-40 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2114).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review