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Closed Constrained Gradient Mining in Retail Databases
June 2006 (vol. 18 no. 6)
pp. 764-769
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.
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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