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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Scalable Model for Mining Critical Least Association Rules
verfasst von : Zailani Abdullah, Tutut Herawan, Mustafa Mat Deris
Erschienen in: Information Computing and Applications
Verlag: Springer Berlin Heidelberg
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A research in mining least association rules is still outstanding and thus requiring more attentions. Until now; only few algorithms and techniques are developed to mine the significant least association rules. In addition, mining such rules always suffered from the high computational costs, complicated and required dedicated measurement. Therefore, this paper proposed a scalable model called Critical Least Association Rule (CLAR) to discover the significant and critical least association rules. Experiments with a real and UCI datasets show that the CLAR can generate the critical least association rules, up to 1.5 times faster and less 100% complexity than benchmarked FP-Growth.