Mining N-most interesting itemsets without support threshold by the COFI-tree
by Sze-Chung Ngan, Tsang Lam, Raymond Chi-Wing Wong, Ada Wai-Chee Fu
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 1, No. 1, 2005

Abstract: Data mining is the discovery of interesting and hidden patterns from a large amount of collected data. Applications can be found in many organisations with large databases, for many different purposes such as customer relationships, marketing, planning, scientific discovery, and other data analysis. In this paper, the problem of mining N-most interesting itemsets is addressed. We make use of the techniques of COFI-tree in order to tackle the problem. In our experiments, we find that our proposed algorithm based on COFI-tree performs faster than the previous approach BOMO based on the FP-tree.

Online publication date: Tue, 05-Jul-2005

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