Closed Constrained Gradient Mining in Retail Databases
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Incorporating constraints into frequent itemset mining not only improves data mining efficiency, but also leads to concise and meaningful results. In this paper, a framework for closed constrained gradient itemset mining in retail databases is proposed by introducing the concept of gradient constraint into closed itemset mining. A tailored version of CLOSET+, LCLOSET, is first briefly introduced, which is designed for efficient closed itemset mining from sparse databases. Then, a newly proposed weaker but antimonotone measure, {\rm{top}}{\hbox{-}}X average measure, is proposed and can be adopted to prune search space effectively. Experiments show that a combination of LCLOSET and the {\rm{top}}{\hbox{-}}X average pruning provides an efficient approach to mining frequent closed gradient itemsets.
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Index Terms:
Data mining, frequent closed itemset, association rule, gradient pattern.
Citation:
Jianyong Wang, Jiawei Han, Jian Pei, "Closed Constrained Gradient Mining in Retail Databases," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 6, pp. 764-769, June 2006, doi:10.1109/TKDE.2006.88
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