Efficient mining of association rules using closed itemset lattices
Information Systems
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
Data Mining and Knowledge Discovery
Fast and Memory Efficient Mining of Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
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Recently, several efficient frequent closed itemset mining methods have been proposed. Those methods are only able to mine relationship among item information. However, real life colleted data in transaction database usually contain many interesting useful information not only item information but also dimension and hierarchal information. Such information can be used for analysis on knowledge discovery in database system. In this paper, we propose a new method, called frequent closed multi-dimensional multi-level pattern mining, which is suitable for mining frequent patterns in real life information. In additions, we show that 1) our completed frequent closed multidimensional multi-level patterns are smaller than the number of multi-dimensional multi-level frequent patterns and 2) our closed multi-dimensional multi-level patterns represent all patterns of multi-dimensional multi-level in equivalence class with the same support.