Efficient Mining of Frequent Patterns from Uncertain Data

Since its introduction, mining of frequent patterns has been the subject of numerous studies. Generally, they focus on improving algorithmic efficiency for finding frequent patterns or on extending the notion of frequent patterns to other interesting patterns. Most of these studies 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 ones are uncertain about the content of transactions. 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 algorithm over its non-tree-based counterpart.