Interactive mining of high utility patterns over data streams

► Devising a new tree structure, called HUS-tree (high utility stream tree), to capture important information from a data stream in a batch-by-batch fashion. ► Development of a novel algorithm, called HUPMS (high utility pattern mining over stream data), for mining high utility patterns over incremental data streams with a sliding window method. ► By using an HUS-tree and exploiting a pattern growth mining approach, HUPMS significantly reduces the execution time and memory usage for stream data processing. ► Description of how to apply our approach for interactive mining over data streams. ► Extensive performance analyses to show that our algorithm is efficient for incremental and interactive high utility pattern mining over data streams with a sliding window, outperforms the existing algorithms, can efficiently handle a large number of distinct items and transactions. High utility pattern (HUP) mining over data streams has become a challenging research issue in data mining. When a data stream flows through, the old information may not be interesting in the current time period. Therefore, incremental HUP mining is necessary over data streams. Even though some methods have been proposed to discover recent HUPs by using a sliding window, they suffer from the level-wise candidate generation-and-test problem. Hence, they need a large amount of execution time and memory. Moreover, their data structures are not suitable for interactive mining. To solve these problems of the existing algorithms, in this paper, we propose a novel tree structure, called HUS-tree (high utility stream tree) and a new algorithm, called HUPMS (high utility pattern mining over stream data) for incremental and interactive HUP mining over data streams with a sliding window. By capturing the important information of stream data into an HUS-tree, our HUPMS algorithm can mine all the HUPs in the current window with a pattern growth approach. Furthermore, HUS-tree is very efficient for interactive mining. Extensive performance analyses show that our algorithm is very efficient for incremental and interactive HUP mining over data streams and significantly outperforms the existing sliding window-based HUP mining algorithms.