Mining Local Periodic Patterns in a Discrete Sequence

Abstract Periodic frequent patterns are sets of events or items that periodically appear in a sequence of events or transactions. Many algorithms have been designed to identify periodic frequent patterns in data. However, most assume that the periodic behavior of a pattern does not change much over time. To address this limitation, this paper proposes to discover a novel type of periodic patterns in a sequence of events or transactions, called Local Periodic Patterns (LPPs) which are patterns (sets of events) that have a periodic behavior in some non predefined time-intervals. A pattern is said to be a local periodic pattern if it appears regularly and continuously in some time-interval(s). Two novel measures are proposed to assess the periodicity and frequency of patterns in time-intervals. The maxSoPer (maximal period of spillovers) measure allows detecting time-intervals of variable lengths where a pattern is continuously periodic, while the minDur (minimal duration) measure ensures that those time-intervals have a minimum duration. To discover all LPPs, the paper presents three efficient algorithms. An experimental evaluation on real datasets shows that the proposed algorithms are efficient and can provide useful patterns that cannot be found using traditional periodic pattern mining algorithms.