A new and versatile method for association generation

Arnihood Amir, Ronen Feldman, Reuven Kashi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations


Current algorithms for finding associations among the attributes describing data in a database have a number of shortcomings: 1 Applications that require associations with very small support have prohibitively large running times. 2 They assume a static database. Some applications require generating associations in real-time from a dynamic database, where transactions are constantly being added and deleted. There are no existing algorithms to accomodate such applications. 3 They can only find associations of the type where a conjunction of attributes implies a conjunction of different attributes. It turns out that there are many cases where a conjunction of attributes implies another conjunction only provided the exclusion of certain attributes. To our knowledge, there is no current algorithm that can generate such excluding associations. We present a novel method for association generation, that answers all three above desiderata. Our method is inherently different from all existing algorithms, and especially suitable to textual databases with binary attributes. At the heart of our algorithm lies the use of subword trees for quick indexing into the required database statistics. We tested our algorithm on the Reuters-22173 database with satisfactory results.

Original languageEnglish
Title of host publicationPrinciples of Data Mining and Knowledge Discovery - 1st European Symposium, PKDD 1997, Proceedings
EditorsJan Komorowski, Jan Zytkow
PublisherSpringer Verlag
Number of pages11
ISBN (Print)3540632239, 9783540632238
StatePublished - 1997
Event1st European Symposium on Principles of Data Mining and Knowledge Discovery, PKDD 1997 - Trondheim, Norway
Duration: 24 Jun 199727 Jun 1997

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


Conference1st European Symposium on Principles of Data Mining and Knowledge Discovery, PKDD 1997

Bibliographical note

Publisher Copyright:
© Springer-Vertag Berlin Heidelberg 1997.


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