| Abstract: | It presented an association rule clustering general framework exploring interestingness of rules. It investigate a general framework for clustering unconstrained association rules over unconstrained domains to enable automating much of the laborious manual effort normally involved in the exploration and understanding of interestingness. We also introduce an algorithm and investigate how this approach can be incorporated into the mining process. The output of the new mining process is significantly reduced, almost by half, making post-processing easier, plus post-processing can often achieve similar results with shorter runtime.
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