12 March 2002 FP-tree approach for mining N-most interesting itemsets
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In classical association rules mining, a minimum support threshold is assumed to be available for mining frequent itemsets. However, setting such a threshold is typically hard. If the threshold is set too high, nothing will be discovered; and if it is set too low, too many itemsets will be generated, which also implies inefficiency. In this paper, we handle a more practical problem, roughly speaking, it is to mine the N k-itemsets with the highest support for k up to a certain kmax value. We call the results the N-most interesting itemsets. Generally, it is more straightforward for users to determine N and kmax. This approach also provides a solution for an open issue in the problem of subspace clustering. However, with the above problem definition without the support threshold, the subset closure property of the apriori-gen algorithm no longer holds. We propose three new algorithms, LOOPBACK, BOLB, and BOMO, for mining N-most interesting itemsets by variations of the FP-tree approach. Experiments show that all our methods outperform the previously proposed Itemset-Loop algorithm.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yin Ling Cheung, Yin Ling Cheung, Ada Wai Chee Fu, Ada Wai Chee Fu, } "FP-tree approach for mining N-most interesting itemsets", Proc. SPIE 4730, Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV, (12 March 2002); doi: 10.1117/12.460253; https://doi.org/10.1117/12.460253


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