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.
Supported by the Natural Science Foundation of China (No. 60402010), Zhejiang Provincial Natural Science Foundation of China (Y105250) and the Science-Technology Progrom of Zhejiang Province of China (No. 2004C31098).
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Wang, J., Xu, C., Dan, H., Pan, Y. (2006). SA-IFIM: Incrementally Mining Frequent Itemsets in Update Distorted Databases. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_9
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DOI: https://doi.org/10.1007/11811305_9
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