Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
Mining Multi-Dimensional Constrained Gradients in Data Cubes
Proceedings of the 27th International Conference on Very Large Data Bases
DualMiner: A Dual-Pruning Algorithm for Itemsets with Constraints
Data Mining and Knowledge Discovery
From Path Tree To Frequent Patterns: A Framework for Mining Frequent Patterns
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Out-of-core coherent closed quasi-clique mining from large dense graph databases
ACM Transactions on Database Systems (TODS)
Mining images using clustering and data compressing techniques
International Journal of Information and Communication Technology
Mining significant change patterns in multidimensional spaces
International Journal of Business Intelligence and Data Mining
Approximate weighted frequent pattern mining with/without noisy environments
Knowledge-Based Systems
Constrained Cube Lattices for Multidimensional Database Mining
International Journal of Data Warehousing and Mining
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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.