Sequential Pattern Mining in Multiple Data Streams Gong Chen, Xindong Wu, and Xingquan Zhu   View Paper (PDF)   Return to Session 4: Time Series II Abstract In this paper, we deal with mining sequential patterns in multiple data streams. Building on a state-of-the-art sequential pattern mining algorithm PrefixSpan for mining transaction databases, we propose MILEš, an efficient algorithm to facilitate the mining process. MILE recursively utilizes the knowledge of existing patterns to avoid redundant data scanning, and can therefore effectively speed up the new patterns' discovery process. Another unique feature of MILE is that it can incorporate some prior knowledge of the data distribution in data streams into the mining process to further improve the performance. Extensive empirical results show thatMILE is significantly faster than PrefixSpan. As MILE consumes more memory than PrefixSpan, we also present a solution to balance the memory usage and time efficiency in memory constrained environments. ©2006 Association for Computing Machinery