Efficient Data Streams Based Closed Frequent Itemsets Mining Algorithm
Online mining of frequent closed itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we proposed a novel sliding window based algorithm. The algorithm exploits lattice properties to limit the search to frequent close itemsets which share at least one item with the new transaction. Experiments results on synthetic datasets show that our proposed algorithm is both time and space efficient.
Xiangdong Zhang, Hongnan Li, Xiating Feng and Zhihua Chen
J. Tan "Efficient Data Streams Based Closed Frequent Itemsets Mining Algorithm", Applied Mechanics and Materials, Vols. 256-259, pp. 2910-2913, 2013