Periodic pattern has been utilized in many real life applications, such as weather conditions in a particular season, transactions in a superstore, power consumption, computer network fault analysis, and analysis of DNA and protein sequences. Periodic pattern mining is a popular though challenging research field in data mining because periodic patterns are of different types (namely full, inner, and tail patterns) and varied periodicities (namely perfect, imperfect, and asynchronous periodicity). Previous periodic pattern mining methods have some disadvantages: (1) Previous methods have to find different patterns separately; (2) They require postprocessing such as level-by-level join strategies for mining complex periodic patterns which have wildcards between two items. They cannot mine full, tail, and inner periodic patterns with perfect, imperfect, and asynchronous periodicities simultaneously. Therefore, an effective and comprehensive approach capable of discovering the above specified kinds of periodic patterns is needed. We propose a novel suffix tree-based algorithm, Mining dIfferent kinds of Periodic Patterns Simultaneously, MIPPS, to address the above issues. MIPPS finds different kinds of periodic patterns with different periodicities simultaneously without level-by-level join techniques using a novel incremental propagation generator. In addition, MIPPS mines periodic patterns efficiently using some pruning strategies. For the performance evaluation, we use both synthetic and real data to confirm good performance and scalability with complex periodic patterns.
All Science Journal Classification (ASJC) codes