ALCOMFT-TR-03-197

Gemma Casas-Garriga
Discovering Unbounded Episodes in Sequential Data
Barcelona. Work packages 1 and 4. December 2003.

Abstract: One basic goal in the analysis of time-series data is to find frequent interesting episodes, i.e, collections of events occurring frequently together in the input sequence. Most widely-known work decide the interestingness of an episode from a fixed user-specified window width or interval, that bounds the length of the subsequent sequential association rules. We present in this paper, a more intuitive definition that allows, in turn, interesting episodes to grow during the mining without any user-specified help. A convenient algorithm to efficiently discover the proposed unbounded episodes is also implemented. Experimental results confirm that our approach results useful and advantageous.

Postscript file: ALCOMFT-TR-03-197.ps.gz (65 kb).