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Integrating classification and association rule mining (1998)
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| Venue: | In Proc of KDD |
| Citations: | 578 - 21 self |
BibTeX
@INPROCEEDINGS{Liu98integratingclassification,
author = {Bing Liu and Wynne Hsu and Yiming Ma},
title = {Integrating classification and association rule mining},
booktitle = {In Proc of KDD},
year = {1998}
}
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Abstract
Classification rule mining aims to discover a small set of rules in the database that forms an accurate classifier. Association rule mining finds all the rules existing in the database that satisfy some minimum support and minimum confidence constraints. For association rule mining, the target of discovery is not pre-determined, while for classification rule mining there is one and only one predetermined target. In this paper, we propose to integrate these two mining techniques. The integration is done by focusing on mining a special subset of association rules, called class association rules (CARs). An efficient algorithm is also given for building a classifier based on the set of discovered CARs. Experimental results show that the classifier built this way is, in general, more accurate than that produced by the state-of-the-art classification system C4.5. In addition, this integration helps to solve a number of problems that exist in the current classification systems.
Keyphrases
association rule mining association rule accurate classifier special subset minimum confidence constraint minimum support class association rule efficient algorithm small set classification rule mining aim current classification system discovered car classification rule mining mining technique experimental result state-of-the-art classification system c4
