Efficient mining of association rules using closed itemset lattices
Information Systems
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Frequent Closed Itemsets for Association Rules
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Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets
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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
Mining Non-Redundant Association Rules
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Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
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IEEE Transactions on Knowledge and Data Engineering
Fast and Memory Efficient Mining of Frequent Closed Itemsets
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ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
IEEE Transactions on Knowledge and Data Engineering
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Frequent Closed Itemset Mining Using Prefix Graphs with an Efficient Flow-Based Pruning Strategy
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
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Data & Knowledge Engineering
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Science of Computer Programming
Mining frequent itemsets from multidimensional databases
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
DBV-Miner: A Dynamic Bit-Vector approach for fast mining frequent closed itemsets
Expert Systems with Applications: An International Journal
Classification based on association rules: A lattice-based approach
Expert Systems with Applications: An International Journal
A new method for mining Frequent Weighted Itemsets based on WIT-trees
Expert Systems with Applications: An International Journal
A space-time trade off for FUFP-trees maintenance
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
A lattice-based approach for mining most generalization association rules
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
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In this paper, we present an application of frequent itemsets lattice (FIL) to mine minimal non-redundant association rules (MNARs) that reduces a lot of time for mining rules. Our method includes two phases: (1) building FIL and (2) mining MNARs from lattice. we extend the structure of FIL by adding one field to consider whether a lattice node is a minimal generator (mG) or not, and another field to consider whether a lattice node is a frequent closed itemset or not. MNARs are only mined from a minimal generator X to frequent closed itemset Y such that X⊂Y. The experiments show that the mining time from FIL is more effective than that from frequent closed itemsets.