A Tree-Based Approach for Frequent Pattern Mining from Uncertain Data

Abstract

Many frequent pattern mining algorithms find patterns from traditional transaction databases, in which the content of each transaction--namely, items--is definitely known and precise. However, there are many real-life situations in which the content of transactions is uncertain. To deal with these situations, we propose a tree-based mining algorithm to efficiently find frequent patterns from uncertain data, where each item in the transactions is associated with an existential probability. Experimental results show the efficiency of our proposed algorithm.