K.-Y. Huang and C.-H. Chang (Taiwan)
Temporal Databases, Data Mining, Knowledge Discovery, Applications
Mining periodic patterns in temporal database is an impor tant data mining problem with many applications. Previ ous studies have considered synchronous periodic patterns where misaligned occurrences are not allowed. However, asynchronous periodic pattern mining has received less at tention and was only been discussed for a sequence of sym bols where each time point contains one event. In this paper, we propose a more general model of asynchronous periodic patterns from a sequence of symbol sets where a time slot can contain multiple events. Three parameters min rep, max dis, and global rep are employed to spec ify the minimum number of repetitions required for a valid segment of non-disrupted pattern occurrences, the maxi mum allowed disturbance between two successive valid segments, and the total repetitions required for a valid se quence. A four-phase algorithm is devised to discover peri odic patterns from a temporal database presented in vertical format. The experiments demonstrate good performance and scalability with large frequent patterns.
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