Indirect association: Mining higher order dependencies in data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

66 Scopus citations

Abstract

This paper introduces a novel pattern called indirect association and examines its utility in various application domains. Existing algorithms for mining associations, such as Apriori, will only discover itemsets that have support above a user-defined threshold. Any itemsets with support below the minimum support requirement are filtered out. We believe that an infrequent pair of items can be useful if the items are related indirectly via some other set of items. In this paper, we propose an algorithm for deriving indirectly associated itempairs and demonstrate the potential application of these patterns in the retail, textual and stock market domains.

Original languageEnglish (US)
Title of host publicationPrinciples of Data Mining and Knowledge Discovery - 4th European Conference, PKDD 2000, Proceedings
EditorsDjamel A. Zighed, Jan Komorowski, Jan Zytkow
PublisherSpringer Verlag
Pages632-637
Number of pages6
ISBN (Print)9783540410669
DOIs
StatePublished - 2000
Event4th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2000 - Lyon, France
Duration: Sep 13 2000Sep 16 2000

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1910
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2000
Country/TerritoryFrance
CityLyon
Period9/13/009/16/00

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2000.

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