Efficient Sequence Pattern Mining Technique for the Removal of Ambiguity in the Interval Patterns Mining


KIPS Transactions on Software and Data Engineering, Vol. 2, No. 8, pp. 565-570, Aug. 2013
10.3745/KTSDE.2013.2.8.565,   PDF Download:

Abstract

Previous researches on mining sequential patterns mainly focused on discovering patterns from the point-based event. Interval events with a time interval occur in the real world that have the start and end point. Existing interval pattern mining methods that discover relationships among interval events based on the Allen operators have some problems. These are that interval patterns having three or more interval events can be interpreted as several meanings. In this paper, we propose the I_TPrefixSpan algorithm, which is an efficient sequence pattern mining technique for removing ambiguity in the Interval Patterns Mining. The proposed algorithm generates event sequences that have no ambiguity. Therefore, the size of generated candidate set can be minimized by searching sequential pattern mining entries that exist only in the event sequence. The performance evaluation shows that the proposed method is more efficient than existing methods.


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Cite this article
[IEEE Style]
H. Kim, P. S. Choi, D. I. Kim, B. H. Hwang, "Efficient Sequence Pattern Mining Technique for the Removal of Ambiguity in the Interval Patterns Mining," KIPS Transactions on Software and Data Engineering, vol. 2, no. 8, pp. 565-570, 2013. DOI: 10.3745/KTSDE.2013.2.8.565.

[ACM Style]
Hwan Kim, Pil Sun Choi, Dae In Kim, and Bu Hyun Hwang. 2013. Efficient Sequence Pattern Mining Technique for the Removal of Ambiguity in the Interval Patterns Mining. KIPS Transactions on Software and Data Engineering, 2, 8, (2013), 565-570. DOI: 10.3745/KTSDE.2013.2.8.565.