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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.

[1] C. Bucila, J. Gehrke, D. Kifer, and W. White, “Dualminer: A Dual-Pruning Algorithm for Itemsets with Constraints,” Data Mining and Knowledge Discovery, vol. 7, pp. 241-272, 2003.
[2] G. Dong, J. Han, J.M.W. Lam, J. Pei, and K. Wang, “Mining Multi-Dimensional Constrained Gradients in Data Cubes,” Proc. 2001 Int'l Conf. Very Large Data Bases (VLDB '01), pp. 321-330, Sept. 2001.
[3] C. Lucchese, S. Orlando, and R. Perego, “DCI-CLOSED: A Fast and Memory Efficient Algorithm to Mine Frequent Itemsets,” Proc. 2004 ICDM Int'l Workshop Frequent Itemset Mining Implementations (FIMI '04), Nov. 2004.
[4] J. Han, J. Pei, and Y. Yin, “Mining Frequent Patterns without Candidate Generation,” Proc. 2000 ACM-SIGMOD Int'l Conf. Management of Data (SIGMOD '00), pp. 1-12, May 2000.
[5] R. Ng, L.V.S. Lakshmanan, J. Han, and A. Pang, “Exploratory Mining and Pruning Optimizations of Constrained Associations Rules,” Proc. 1998 ACM-SIGMOD Int'l Conf. Management of Data (SIGMOD '98), pp. 13-24, June 1998.
[6] J. Pei, J. Han, and L.V.S. Lakshmanan, “Mining Frequent Itemsets with Convertible Constraints,” Proc. 2001 Int'l Conf. Data Eng. (ICDE '01), pp. 433-442, Apr. 2001.
[7] T. Uno, M. Kiyomi, and H. Arimura, “LCM ver. 2: Efficient Mining Algorithms for Frequent/Closed/Maximal Itemsets,” Proc. 2004 ICDM Int'l Workshop Frequent Itemset Mining Implementations (FIMI '04), Nov. 2004.
[8] J. Wang, J. Han, and J. Pei, “CLOSET+: Searching for the Best Strategies for Mining Frequent Closed Itemsets,” Proc. 2003 ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '03), pp. 236-245, Aug. 2003.
[9] Y. Xu, J.X. Yu, G. Liu, and H. Lu, “From Path Tree to Frequent Patterns: A Framework for Mining Frequent Patterns,” Proc. 2002 Int'l Conf. Data Mining (ICDM '02), pp. 514-521, Dec. 2002.
[10] M.J. Zaki and C.J. Hsiao, “CHARM: An Efficient Algorithm for Closed Itemset Mining,” Proc. 2002 SIAM Int'l Conf. Data Mining (SDM '02), pp. 457-473, Apr. 2002.

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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