Mining a class of complex episodes in event sequences

Authors:
H. K. Dai;G. Wang
Affiliations:
Computer Science Department, Oklahoma State University, Stillwater, Oklahoma;Computer Science Department, Oklahoma State University, Stillwater, Oklahoma
Venue:
AAIM'05 Proceedings of the First international conference on Algorithmic Applications in Management
Year:
2005

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Abstract

This work extends the existing parallel- and serial-episode data mining algorithms to that for parallel connection of serial (PoS) episodes. The PoS-episodes can model more general situations and preserve the sequence information as well. The PoS-episode mining algorithm can provide episode-mining users a powerful mining tool and make the episode mining more flexible. To use the PoS-episode mining algorithm, users need to decide reasonable parameters like window width and minimum frequency ratio. Concepts and methods are provided by using Web log mining as example to illustrate the applicability of the PoS-episode mining and show how to decide reasonable parameters as well as evaluate the mining process.