Mining Temporal Patterns from Sequence Database of Interval-Based Events
Sequential pattern mining is one of the important techniques of data mining to discover some potential useful knowledge from large databases. However, existing approaches for mining sequential patterns are designed for point-based events. In many applications, the essence of events are interval-based, such as disease suffered, stock price increase or decrease, chatting etc. This paper presents a new algorithm to discover temporal pattern from temporal sequences database consisting of interval-based events.
KeywordsTemporal Pattern Event Type Sequential Pattern Temporal Sequence Pattern Mining
Unable to display preview. Download preview PDF.
- 1.Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Proceedings of the 11th International Conference on Data Engineering, pp. 3–14 (1995)Google Scholar
- 3.Han, J., Pei, J., Mortazavi-Asl, B., Chen, Q., Dayal, U., Hsu, M.-C.: Freespan: frequent pattern-projected sequential pattern mining. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining, pp. 355–359 (2000)Google Scholar
- 5.Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.-C.: PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. In: Proceedings of the 17th International Conference on Data Engineering, pp. 215–224 (2001)Google Scholar
- 8.Zhao, Q., Bhowmick, S.S.: Sequential pattern mining: a survey. Technical Report, CAIS, Nanyang Technological University, Singapore (2003)Google Scholar