Mining of Frequent Itemsets from Streams of Uncertain Data

Frequent itemset mining plays an essential role in the mining of various patterns and is in demand in many real-life applications. Hence, mining of frequent itemsets has been the subject of numerous studies since its introduction. Generally, most of these studies find frequent itemsets 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. This calls for the mining of uncertain data. Moreover, due to advances in technology, a flood of precise or uncertain data can be produced in many situations. This calls for the mining of data streams. To deal with these situations, we propose two tree-based mining algorithms to efficiently find frequent itemsets from streams of uncertain data, where each item in the transactions in the streams is associated with an existential probability. Experimental results show the effectiveness of our algorithms in mining frequent itemsets from streams of uncertain data.