Mining Regular Patterns in Data Streams

Abstract

Discovering interesting patterns from high-speed data streams is a challenging problem in data mining. Recently, the support metric-based frequent pattern mining from data stream has achieved a great attention. However, the occurrence frequency of a pattern may not be an appropriate criterion for discovering meaningful patterns. Temporal regularity in occurrence behavior can be a key criterion for assessing the importance of patterns in several online applications such as market basket analysis, gene data analysis, network monitoring, and stock market. A pattern can be said regular if its occurrence behavior satisfies a user-given interval in the data steam. Mining regular patterns from static databases has recently been addressed. However, even though mining regular patterns from stream data is extremely required in online applications, no such algorithm has been proposed yet. Therefore, in this paper we develop a novel tree structure called Regular Pattern Stream tree (RPS-tree), and an efficient mining technique for discovering regular patterns over data stream. Using a sliding window method the RPS-tree captures the stream content, and with an efficient tree updating mechanism it constantly processes exact stream data when the stream flows. Extensive experimental analyses show that our RPS-tree is highly efficient in discovering regular patterns from a high-speed data stream.