TY - JOUR
T1 - Closed constrained gradient mining in retail databases
AU - Wang, Jianyong
AU - Han, Jiawei
AU - Pei, Jian
N1 - Funding Information:
The work was supported in part by the US National Science Foundation NSF IIS-02-09199/IIS-03-08215. Jianyong Wang was supported in part by the National Natural Science Foundation of China (NSFC) under Grant No. 60573061. Jian Pei was supported in part by NSERC Discovery Grant and the US National Science Foundation NSF IIS-0308001. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the funding agencies.
PY - 2006/6
Y1 - 2006/6
N2 - 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, top-X average measure, is proposed and can be adopted to prune search space effectively. Experiments show that a combination of LCLOSET and the top-X average pruning provides an efficient approach to mining frequent closed gradient itemsets.
AB - 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, top-X average measure, is proposed and can be adopted to prune search space effectively. Experiments show that a combination of LCLOSET and the top-X average pruning provides an efficient approach to mining frequent closed gradient itemsets.
KW - Association rule
KW - Data mining
KW - Frequent closed itemset
KW - Gradient pattern
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U2 - 10.1109/TKDE.2006.88
DO - 10.1109/TKDE.2006.88
M3 - Article
AN - SCOPUS:33646407818
SN - 1041-4347
VL - 18
SP - 764
EP - 769
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 6
ER -