Efficient mining of association rules with item constraints

S. Tseng*

*Corresponding author for this work

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


Discovering association rules is a problem of data mining on which numerous studies have been made. An example of an association rule is: “25 percent of transactions that contain beer also contain diapers; 5 percent of all transactions contain both items”. Here, 25 percent is called the confidence of the rule, and 5 percent the support of the rule. Most existing work on this problem focused on finding the association rules between all items in a large database which satisfy user-specified minimum confidence and support. In practice, users are often interested in finding association rules involving only some specified items rather than all items in a query. Meanwhile, based on the searched results in former queries, users might change the minimum confidence and support requirements to obtain suitable number of rules. In this scenario, the users tend to make consecutive queries on interested items with expected quick response rather than wait a long time for getting a lot of association rules between all itemsets in partial which the users are only interested
Original languageEnglish
Title of host publicationDiscovery Science - 1st International Conference, DS 1998, Proceedings
EditorsSetsuo Arikawa, Hiroshi Motoda
PublisherSpringer Verlag
Number of pages2
ISBN (Print)3540653902, 9783540653905
StatePublished - 1 Jan 1998
Event1st International Conference on Discovery Science, DS 1998 - Fukuoka, Japan
Duration: 14 Dec 199816 Dec 1998

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 International Conference on Discovery Science, DS 1998

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