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A New Fuzzy Clustering Validity Index Based on Fuzzy Proximity Matrices
Conference proceeding

A New Fuzzy Clustering Validity Index Based on Fuzzy Proximity Matrices

Rafael Xavier Valente, Antonio Padua Braga and Witold Pedrycz
2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence, pp.489-494
09/2013
DOI: https://doi.org/10.1109/BRICS-CCI-CBIC.2013.87

Abstract

Clustering algorithms Clustering Validation Index Equations Fuzzy Clustering Indexes Iris Linear programming Partitioning algorithms Proximity Matrix Symmetric matrices
This paper presents a new validity index for fuzzy partitions generated by the fuzzy c-means algorithm. The proposed validity index is based on the calculation of factors from the proximity matrix generated from the membership matrix generated by a fuzzy clustering partition algorithm, such as FCM. The experimental results show that the proposed approach is consistent with other well-known metrics and with the dataset structure as observed from Proximity Matrices.

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