Abstract
Research in the field of knowledge discovery from temporal data recently focused on a new type of data: interval sequences. In contrast to event sequences interval sequences contain labeled events with a temporal extension. Mining frequent temporal patterns from interval sequences proved to be a valuable tool for generating knowledge in the automotive business. In this paper we propose a new algorithm for mining frequent temporal patterns from interval sequences: FSMTree. FSMTree uses a prefix tree data structure to efficiently organize all finite state machines and therefore dramatically reduces execution times. We demonstrate the algorithm’s performance on field data from the automotive business.KeywordsTemporal PatternState MachineTemporal IntervalAssociation RuleSupport EvaluationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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