SA-IFIM: Incrementally Mining Frequent Itemsets in Update Distorted Databases

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 datamining 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 in

• Type: Conference
• Year: 2006
• Where: ADMA
• Authors: Jinlong Wang, Congfu Xu, Hongwei Dan, Yunhe Pan
• Keywords: ADMA 2006, Data Mining, Frequent Itemset Mining, Frequent Itemsets, Update Distorted Databases