Interactive mining of top-K frequent closed itemsets from data streams
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摘要
Mining closed frequent itemsets from data streams is of interest recently. However, it is not easy for users to determine a proper minimum support threshold. Hence, it is more reasonable to ask users to set a bound on the result size. Therefore, an interactive single-pass algorithm, called TKC-DS (top-K frequent closed itemsets of data streams), is proposed for mining top-K closed itemsets from data streams efficiently. A novel data structure, called CIL (closed itemset lattice), is developed for maintaining the essential information of closed itemsets generated so far. Experimental results show that the proposed TKC-DS algorithm is an efficient method for mining top-K frequent itemsets from data streams.
论文关键词:Data mining,Data streams,Frequent closed itemsets,Top-K pattern mining,Interactive mining
论文评审过程:Available online 16 February 2009.
论文官网地址:https://doi.org/10.1016/j.eswa.2009.02.012