Frequent itemset mining of uncertain data streams using the damped window model

With advances in technology, large amounts of streaming data can be generated continuously by sensors in applica-tions like environment surveillance. Due to the inherited limitation of sensors, these continuous data can be uncer-tain. This calls for stream mining of uncertain data. In recent years, tree-based algorithms have been proposed to use the sliding window model for mining frequent itemsets from streams of uncertain data. Besides the sliding window model, there are other window models for processing data streams. In this paper, we propose tree-based algorithms that use the damped window model to mine frequent item-sets from streams of uncertain data.