Indirect association: Mining higher order dependencies in data

Pang Ning Tan, Vipin Kumar, Jaideep Srivastava

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

58 Citations (Scopus)

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
Title of host publicationPrinciples of Data Mining and Knowledge Discovery - 4th European Conference, PKDD 2000, Proceedings
PublisherSpringer Verlag
Pages632-637
Number of pages6
Volume1910
ISBN (Print)9783540410669
Publication statusPublished - 2000
Externally publishedYes
Event4th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2000 - Lyon, France
Duration: 13 Sep 200016 Sep 2000

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1910
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other4th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2000
CountryFrance
CityLyon
Period13/9/0016/9/00

Fingerprint

Mining
Higher Order
Stock Market
Requirements
Demonstrate
Financial markets

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Tan, P. N., Kumar, V., & Srivastava, J. (2000). Indirect association: Mining higher order dependencies in data. In Principles of Data Mining and Knowledge Discovery - 4th European Conference, PKDD 2000, Proceedings (Vol. 1910, pp. 632-637). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1910). Springer Verlag.

Indirect association : Mining higher order dependencies in data. / Tan, Pang Ning; Kumar, Vipin; Srivastava, Jaideep.

Principles of Data Mining and Knowledge Discovery - 4th European Conference, PKDD 2000, Proceedings. Vol. 1910 Springer Verlag, 2000. p. 632-637 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1910).

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

Tan, PN, Kumar, V & Srivastava, J 2000, Indirect association: Mining higher order dependencies in data. in Principles of Data Mining and Knowledge Discovery - 4th European Conference, PKDD 2000, Proceedings. vol. 1910, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1910, Springer Verlag, pp. 632-637, 4th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2000, Lyon, France, 13/9/00.
Tan PN, Kumar V, Srivastava J. Indirect association: Mining higher order dependencies in data. In Principles of Data Mining and Knowledge Discovery - 4th European Conference, PKDD 2000, Proceedings. Vol. 1910. Springer Verlag. 2000. p. 632-637. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Tan, Pang Ning ; Kumar, Vipin ; Srivastava, Jaideep. / Indirect association : Mining higher order dependencies in data. Principles of Data Mining and Knowledge Discovery - 4th European Conference, PKDD 2000, Proceedings. Vol. 1910 Springer Verlag, 2000. pp. 632-637 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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