Generating Concise Association Rules
Association rule mining has made many achievements in the area of knowledge discovery. However, the quality of the extracted association rules is a big concern. One problem with the quality of the extracted association rules is the huge size of the extracted rule set. As a matter of fact, very often tens of thousands of association rules are extracted among which many are redundant thus useless. Mining non-redundant rules is a promising approach to solve this problem. The Min-max exact basis proposed by Pasquier et al [Pasquier05] has showed exciting results by generating only non-redundant rules. In this paper, we first propose a relaxing definition for redundancy under which the Min-max exact basis still contains redundant rules; then we propose a condensed representation called Reliable exact basis for exact association rules. The rules in the Reliable exact basis are not only non-redundant but also more succinct than the rules in Min-max exact basis. We prove that the redundancy eliminated by the Reliable exact basis does not reduce the belief to the Reliable exact basis. The size of the Reliable exact basis is much smaller than that of the Min-max exact basis. Moreover, the Reliable exact basis is a lossless representation of exact association rules since we prove that all exact association rules can be deduced from the Reliable exact basis. Experimental results show that the Reliable exact basis significantly reduces the number of the extracted rules.
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|Item Type:||Conference Paper|
|Additional Information:||For more information, please refer to the conference's website (see hypertext link) or contact the author.|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Information Systems not elsewhere classified (080699)|
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Artificial Intelligence and Image Processing not elsewhere classified (080199)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Copyright Owner:||Copyright 2007 Association for Computing Machinery (ACM)|
|Deposited On:||10 Jun 2008|
|Last Modified:||29 Feb 2012 23:34|
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