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\iteman{io-port 05109813}
\itemau{Wang, Jianyong; Han, Jiawei; Pei, Jian}
\itemti{Closed Constrained Gradient Mining in Retail Databases.}
\itemso{IEEE Transactions on Knowledge and Data Engineering 18, No.06, 764-769 (2006).}
\itemab
Summary: 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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\itemcc{}
\itemut{gradient pattern.}
\itemli{doi:10.1109/TKDE.2006.88}
\end