Mining informative rule set for prediction

Article

Li, Jiuyong, Shen, Hong and Topor, Rodney. 2004. "Mining informative rule set for prediction." Journal of Intelligent Information Systems. 22 (2), pp. 155-174. https://doi.org/10.1023/B:JIIS.0000012468.25883.a5
Article Title

Mining informative rule set for prediction

ERA Journal ID18019
Article CategoryArticle
AuthorsLi, Jiuyong (Author), Shen, Hong (Author) and Topor, Rodney (Author)
Journal TitleJournal of Intelligent Information Systems
Journal Citation22 (2), pp. 155-174
Year2004
Place of PublicationNetherlands
ISSN0925-9902
1573-7675
Digital Object Identifier (DOI)https://doi.org/10.1023/B:JIIS.0000012468.25883.a5
Web Address (URL)http://springerlink.metapress.com/content/4u055l3yylfw/?p=17aaa6c63bc744068edb108ffe6f0d5e&pi=20
Abstract

[Abstract]: Mining transaction databases for association rules usually generates a large number of rules, most of which are unnecessary when used for subsequent prediction. In this paper we define a rule set for a given transaction database that is much smaller than the association rule set but makes the same predictions as the association rule set by the confidence priority. We call this rule set informative rule set. The informative rule set is not constrained to particular target items; and it is smaller than the non-redundant association rule set. We characterise relationships between the informative rule set and non-redundant association rule set. We present an algorithm to directly generate the informative rule set without generating all frequent itemsets first that accesses the database less frequently than other direct methods. We show experimentally that the informative rule set is much smaller and can be generated more efficiently than both the association rule set and non-redundant association rule set.

Keywordsassociation rule mining; data mining; prediction; information rule set
ANZSRC Field of Research 2020469999. Other information and computing sciences not elsewhere classified
Public Notes

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Byline AffiliationsDepartment of Mathematics and Computing
Japan Advanced Institute of Science and Technology, Japan
Griffith University
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