Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Speed-up Iterative Frequent Itemset Mining with Constraint Changes
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Generating a Condensed Representation for Association Rules
Journal of Intelligent Information Systems
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
Efficient Algorithms for Mining Frequent Itemsets with Constraint
KSE '11 Proceedings of the 2011 Third International Conference on Knowledge and Systems Engineering
An efficient method for mining frequent itemsets with double constraints
Engineering Applications of Artificial Intelligence
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Mining frequent itemsets can often generate a large number of frequent itemsets. Recent studies proposed mining itemset with the different types of constraint. The paper is to mine frequent itemsets, where a one: does not contain any item of C0 or contains at least one item of C0. The set of all those ones is partitioned into equivalence classes. Without loss of generality, we only investigate each class independently. One class is represented by a frequent closed set L and splits into two disjoint sub-classes. The first contains frequent itemsets that do not contain any item of C0. It is generated from the corresponding generators. The second includes in two subsets of the frequent itemsets coming from the generators containing in C0, and the ones obtained by connecting each non-empty subset of L(C0 with each element of the first.