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.
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Kempe, S., Hipp, J., Kruse, R. (2008). FSMTree: An Efficient Algorithm for Mining Frequent Temporal Patterns. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds) Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78246-9_30
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DOI: https://doi.org/10.1007/978-3-540-78246-9_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78239-1
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