Author： Song Wei
Publisher： Taylor & Francis Ltd
ISSN： 1362-3079
Source： Journal of Experimental & Theoretical Artificial Intelligence, Vol.21, Iss.4, 2009-12, pp. : 259-272
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Abstract
The sheer size of all frequent itemsets is one challenging problem in data mining research. Based on both closed itemset and maximal itemset, meta itemset which is a new concise representation of frequent itemset is proposed. It is proved that both closed itemset and maximal itemset are special cases of meta itemset. The set of all closed itemsets and the set of all maximal itemsets form the upper bound and the lower bound of the set of all meta itemsets. Then, property and pruning strategies of meta itemset are discussed. Finally, an efficient algorithm for mining meta itemset is proposed. Experimental results show that the proposed algorithm is effective and efficient.
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