An improving mining algorithm aiming at a kind of specific function of degree of interest
Tianrui Li, Jun Ma, Yang Xu · 2003
Association rule mining is an important research area in data mining, which has a broad application background. There exist two short-comings in the classical association rule mining problem, namely every itemset is treated equivalently and use a uniform minimum support and minimum conference as weighting standard. Through introducing the function of the degree of interest on itemsets, /spl phi/, a direct generation of the problem of association rule mining, called /spl phi/-association rule mining, was introduced in Li (2001). It can solve the two short-comings at the same time. Based on an FP-tree, a universal algorithm for mining a T-frequent closed itemset was proposed in Li et al. (2001). Because this algorithm is based on general definition of /spl phi/, it will not perform well for all /spl phi/. In this paper, aiming at a kind of specific /spl phi/, namely, the degree of interest of every item is given, we present an improved algorithm. The experimental and performance studies show that our algorithm is more efficient than previous algorithm.