A geometric approach for mining sequential patterns in interval-based data streams

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. The majority of sequential pattern mining approaches dealt with such activities as a single point event and thus lost valuable information in the collected patterns. Recently, some approaches have carefully considered this interval-based nature of the events, but they have major limitations. They concentrate only on the order of events without taking the durations of the gaps between them into account and usually employ a binary representation to describe patterns. To resolve these problems, we propose the PIVOTMiner, an interval-based data mining algorithm using a geometric representation approach of intervals. Noisy events can be served with the geometric representation and a fuzzy set can be retrieved from the geometric patterns. PIVOTMiner can flexibly work on data presented as any number of not necessarily aligned interval sequences and in particular can utilize data presented as single interval sequence stream without the need to create samples. Our experimental results on both synthetic and real-world smart home datasets show that the information presented in our mined patterns are richer than those of most state-of-the-art algorithms while spending considerably smaller running times.