Efficient mining of association rules using closed itemset lattices
Information Systems
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
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
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
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
Fast and Memory Efficient Mining of Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
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Incrementality is a major challenge in the mining of dynamic databases. In such databases, the maintenance of association rules can be directly mapped into the problem of maintaining closed frequent itemsets. A number of incremental strategies have been proposed earlier with several limitations. A serious limitation is the need to examine the entire family of closed itemsets, whenever there are insertions or deletions in the database. The proposed strategy relies on an efficient and selective update of the closed itemsets using an indexed trie structure. The framework emphasizes on certain fundamental and structural properties of Galois Lattice theory to overcome the limitations of the earlier approaches. Apart from facilitating a selective update, the indexed structure removes the necessity of working with a wholly memory resident trie.