Asynchronous Periodic Pattern Mining from Multi-event Time Series Databases
Author: K.-Y. Huang, C.-H. Chang
Publish Year: 2004-02-17
Update by: March 31, 2025
摘要
Mining periodic patterns in temporal database is an important data mining problem with many applications. Previous studies have considered synchronous periodic patterns where misaligned occurrences are not allowed. However, asynchronous periodic pattern mining has received less attention and was only been discussed for a sequence of symbols 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 minrep, maxdis, and global rep are employed to specify the minimum number of repetitions required for a valid segment of non-disrupted pattern occurrences, the maximum allowed disturbance between two successive valid segments, and the total repetitions required for a valid sequence. A four-phase algorithm is devised to discover periodic patterns from a temporal database presented in vertical format. The experiments demonstrate good performance and scalability with large frequent patterns.