Towards an Efficient Ranking of Interval-Based Patterns

Almost all activities observed in nowadays applications are correlated with a timing sequence. Users are mainly looking for interesting sequences out of such data. Sequential pattern mining algorithms aim at finding frequent sequences. Usually, the mined activities have timing durations that represent time intervals between their starting and ending points. Most sequential pattern mining approaches dealt with such activities as a single point event and thus lost many valuable information in the collected patterns. We present the PIVOTMiner, an efficient interval-based sequential pattern mining algorithm using a geometric representation of intervals. The interestingness level is not necessarily positively correlated with the frequency of the patterns. In many applications, users are seeking for rare patterns that considerably deviate from the majority. Simply delivering the bottom-k patterns does not guarantee their high outlierness (or deviation) from the frequent ones. We propose additionally the PIVOTRanker, the first scalable algorithm for ranking rare interval-based sequential patterns based on their outlierness. Our experimental results on both synthetic and real-world datasets show that PIVOTMiner spends considerably less time than two state-of-the-art competitors, and that PIVOTRanker delivers a meaningful and useful ranking of rare patterns.