FCHM-stream: fast closed high utility itemsets mining over data streams

Abstract

The high-speed, continuous and endless characteristics of data streams make it a challenging task to quickly mine high utility itemsets in limited memory space. The sliding window model, which focuses only on the most recent data, has received extensive research and attention as it can effectively adapt to the data stream environment. However, the presence of many communal batches in adjacent sliding windows causes the algorithm to repeatedly generate a large number of identical itemsets, which reduces the spatiotemporal performance of the algorithm. In order to solve these problems and provide users with a concise and lossless resultset, a new closed high utility pattern mining algorithm over data stream is proposed, named FCHM-Stream. A new utility list structure based on batch division and a resultset maintenance strategy based on skip-list structure are designed to effectively reduce identical itemsets repeatedly generated and thus reduce the running time of the algorithm. Extensive experimental results show that the proposed algorithm has a large improvement in runtime compared to the state-of-the-art algorithms.