Mining Frequent Itemsets from Uncertain Data

Abstract. We study the problem of mining frequent itemsets from uncertain data under a probabilistic framework. We consider transactions whose items are associated with existential probabilities and give a formal definition of frequent patterns under such an uncertain data model. We show that traditional algorithms for mining frequent itemsets are either inapplicable or computationally inefficient under such a model. A data trimming framework is proposed to improve mining efficiency. Through extensive experiments, we show that the data trimming technique can achieve significant savings in both CPU cost and I/O cost.

• Type: Conference
• Year: 2007
• Where: PAKDD
• Authors: Chun Kit Chui, Ben Kao, Edward Hung
• Keywords: Data Mining, Mining Frequent Itemsets, PAKDD 2007, Uncertain Data, Uncertain Data Model