Z. Jan, S. Bashir, and A.R. Baig (Pakistan)
Data Mining, Association Rules Mining, Frequent Itemset Mining, N-most interesting itemset mining, Bit-vector representation approach, Bit-vector Projection.
Real world datasets are sparse, dirty and contain hundreds of items. In such situations, discovering interesting rules (results) using traditional frequent itemset mining approach by specifying a user defined input support threshold is not appropriate. Since without any domain knowledge, setting support threshold small or large can output nothing or a large number of redundant uninteresting results. Recently a novel approach of mining N-most interesting itemsets is proposed, which discovers only top N interesting results without specifying any user defined support threshold. However, mining N-most interesting itemsets are more costly in terms of itemset search space exploration and processing cost. Thereby, the efficiency of mining process highly depends upon the itemset frequency (support) counting, implementation techniques and projection of relevant transactions to lower level nodes of search space. In this paper, we present a novel N-most interesting itemset mining algorithm (N-MostMiner) using the bit-vector representation approach which is very efficient in terms of itemset frequency counting and transactions projection. Several efficient implementation techniques of N MostMiner are also present which we experienced in our implementation. Our different experimental results on benchmark datasets suggest that the N-MostMiner is very efficient in terms of processing time as compared to currently best algorithm BOMO.
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