FUZZY MULTIPLE-LEVEL SEQUENTIAL PATTERNS DISCOVERY FROM CUSTOMER TRANSACTION DATABASES

An Chen, Huilin Ye

Abstract

Sequential pattern discovery is a very important research topic in data mining and knowledge discovery and has been widely applied in business analysis. Previous works were focused on mining sequential patterns at a single concept level based on definite and accurate concept which may not be concise and meaningful enough for human experts to easily obtain nontrivial knowledge from the rules discovered. In this paper, we introduce concept hierarchies firstly, and then discuss a mining algorithm F-MLSPDA for discovering multiple-level sequential patterns with quantitative attribute based on fuzzy partitions.

References

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Paper Citation


in Harvard Style

Chen A. and Ye H. (2004). FUZZY MULTIPLE-LEVEL SEQUENTIAL PATTERNS DISCOVERY FROM CUSTOMER TRANSACTION DATABASES . In Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 972-8865-00-7, pages 434-440. DOI: 10.5220/0002608604340440


in Bibtex Style

@conference{iceis04,
author={An Chen and Huilin Ye},
title={FUZZY MULTIPLE-LEVEL SEQUENTIAL PATTERNS DISCOVERY FROM CUSTOMER TRANSACTION DATABASES},
booktitle={Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2004},
pages={434-440},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002608604340440},
isbn={972-8865-00-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - FUZZY MULTIPLE-LEVEL SEQUENTIAL PATTERNS DISCOVERY FROM CUSTOMER TRANSACTION DATABASES
SN - 972-8865-00-7
AU - Chen A.
AU - Ye H.
PY - 2004
SP - 434
EP - 440
DO - 10.5220/0002608604340440