FSMTree: An Efficient Algorithm for Mining Frequent Temporal Patterns

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.

• Type: Conference
• Year: 2007
• Where: GFKL
• Authors: Steffen Kempe, Jochen Hipp, Rudolf Kruse
• Keywords: Data Mining, Frequent Temporal Patterns, GFKL 2007, Interval Sequences, Temporal Data