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Multi-objective genetic algorithm based approach for optimizing fuzzy sequential patterns

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2 Author(s)
Kaya, M. ; Dept. of Comput. Eng., Firat Univ., Elazig, Turkey ; Alhaji, R.

This work introduces the optimized sequential pattern problem and presents a novel approach to find such patterns. All the methods described in the literature to optimize association rules employ a single objective measure, such as optimized confidence or optimized support. In this study, we propose a novel multiobjective genetic algorithm (GA) based optimization method for optimizing quantitative sequential patterns. The objective measures of are support, confidence and a parameter related to the total number of fuzzy sets in the sequence. Experimental results on a synthetic database demonstrate the effectiveness and applicability of the proposed method.

Published in:

Tools with Artificial Intelligence, 2004. ICTAI 2004. 16th IEEE International Conference on

Date of Conference:

15-17 Nov. 2004