Using an interest ontology for improved support in rule mining

Xiaoming Chen; Xuan Zhou; Richard Scherl; James Geller

doi:10.1007/978-3-540-45228-7_32

Using an interest ontology for improved support in rule mining

Xiaoming Chen, Xuan Zhou, Richard Scherl, James Geller

Research output: Chapter in Book/Report/Conference proceeding › Chapter

11 Scopus citations

Abstract

This paper describes the use of a concept hierarchy for improving the results of association rule mining. Given a large set of tuples with demographic information and personal interest information, association rules can be derived, that associate ages and gender with interests. However, there are two problems. Some data sets are too sparse for coming up with rules with high support. Secondly, some data sets with abstract interests do not represent the actual interests well. To overcome these problems, we are preprocessing the data tuples using an ontology of interests. Thus, interests within tuples that are very specific are replaced by more general interests retrieved from the interest ontology. This results in many more tuples at a more general level. Feeding those tuples to an association rule miner results in rules that have better support and that better represent the reality.

Original language	English (US)
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Yahiko Kambayashi, Mukesh Mohania, Wolfram Wob
Publisher	Springer Verlag
Pages	320-329
Number of pages	10
ISBN (Print)	354040807X
DOIs	https://doi.org/10.1007/978-3-540-45228-7_32
State	Published - 2003

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2737
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

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10.1007/978-3-540-45228-7_32

Cite this

Chen, X., Zhou, X., Scherl, R., & Geller, J. (2003). Using an interest ontology for improved support in rule mining. In Y. Kambayashi, M. Mohania, & W. Wob (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 320-329). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2737). Springer Verlag. https://doi.org/10.1007/978-3-540-45228-7_32

Chen, Xiaoming ; Zhou, Xuan ; Scherl, Richard et al. / Using an interest ontology for improved support in rule mining. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Yahiko Kambayashi ; Mukesh Mohania ; Wolfram Wob. Springer Verlag, 2003. pp. 320-329 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Chen, X, Zhou, X, Scherl, R & Geller, J 2003, Using an interest ontology for improved support in rule mining. in Y Kambayashi, M Mohania & W Wob (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2737, Springer Verlag, pp. 320-329. https://doi.org/10.1007/978-3-540-45228-7_32

Using an interest ontology for improved support in rule mining. / Chen, Xiaoming; Zhou, Xuan; Scherl, Richard et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Yahiko Kambayashi; Mukesh Mohania; Wolfram Wob. Springer Verlag, 2003. p. 320-329 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2737).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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AB - This paper describes the use of a concept hierarchy for improving the results of association rule mining. Given a large set of tuples with demographic information and personal interest information, association rules can be derived, that associate ages and gender with interests. However, there are two problems. Some data sets are too sparse for coming up with rules with high support. Secondly, some data sets with abstract interests do not represent the actual interests well. To overcome these problems, we are preprocessing the data tuples using an ontology of interests. Thus, interests within tuples that are very specific are replaced by more general interests retrieved from the interest ontology. This results in many more tuples at a more general level. Feeding those tuples to an association rule miner results in rules that have better support and that better represent the reality.

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Chen X, Zhou X, Scherl R, Geller J. Using an interest ontology for improved support in rule mining. In Kambayashi Y, Mohania M, Wob W, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2003. p. 320-329. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-45228-7_32

Using an interest ontology for improved support in rule mining

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