SA-IFIM: incrementally mining frequent itemsets in update distorted databases

Authors:
Jinlong Wang;Congfu Xu;Hongwei Dan;Yunhe Pan
Affiliations:
Institute of Artificial Intelligence, Zhejiang University, Hangzhou, China;Institute of Artificial Intelligence, Zhejiang University, Hangzhou, China;Institute of Artificial Intelligence, Zhejiang University, Hangzhou, China;Institute of Artificial Intelligence, Zhejiang University, Hangzhou, China
Venue:
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Year:
2006

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Abstract

The issue of maintaining privacy in frequent itemset mining has attracted considerable attentions. In most of those works, only distorted data are available which may bring a lot of issues in the data-mining process. Especially, in the dynamic update distorted database environment, it is nontrivial to mine frequent itemsets incrementally due to the high counting overhead to recompute support counts for itemsets. This paper investigates such a problem and develops an efficient algorithm SA-IFIM for incrementally mining frequent itemsets in update distorted databases. In this algorithm, some additional information is stored during the earlier mining process to support the efficient incremental computation. Especially, with the introduction of supporting aggregate and representing it with bit vector, the transaction database is transformed into machine oriented model to perform fast support computation. The performance studies show the efficiency of our algorithm.