Genetic fuzzy clustering by means of discovering membership functions

Date

1997-01-01

Author

Turhan, M

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It has been observed that in the previous Genetic Algorithms (GA) based Fuzzy Clustering (FC) works only some of the parameters of an FC system are developed. Here, a new approach is proposed to develop directly the membership functions for the clusters using GA. This new technique is implemented and tested on common test data. A comparative study of the results against the quotations in literature reveals that the standard c-means FC technique is outperformed by the proposed technique in the count of misclassifications aspect.

URI

https://hdl.handle.net/11511/63671

Journal

ADVANCES IN INTELLIGENT DATA ANALYSIS

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Department of Computer Engineering, Article

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Citation Formats

M. Turhan, “Genetic fuzzy clustering by means of discovering membership functions,” ADVANCES IN INTELLIGENT DATA ANALYSIS, pp. 383–393, 1997, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/63671.