Meta-learning for post-processing of association rules
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
Mining and post-processing of association rules in the atherosclerosis risk domain
ITBAM'10 Proceedings of the First international conference on Information technology in bio- and medical informatics
Behavior-based clustering and analysis of interestingness measures for association rule mining
Data Mining and Knowledge Discovery
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Determining interestingness is a notoriously difficultproblem: it is subjective and elusive to capture. It is alsobecoming an increasingly more important problem in KDDas the number of mined patterns increases. In this work weintroduce and investigate a framework for association ruleclustering that enables automating much of the laboriousmanual effort normally involved in the exploration and understandingof interestingness. Clustering is ideally suitedfor this task; it is the unsupervised organization of patternsinto groups, so that patterns in the same group are moresimilar to each other than to patterns in other groups. Wealso define a data-driven inferred labeling of these clusters,the ancestor coverage, which provides an intuitive, conciserepresentation of the clusters.