A Fuzzy Clustering Model for Fuzzy Data with Outliers

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Author(s): M. H. Fazel Zarandi (Amirkabir University of Technology, Iran)and Zahra S. Razaee (Amirkabir University of Technology, Iran)
Copyright: 2013
Pages: 12
Source title: Contemporary Theory and Pragmatic Approaches in Fuzzy Computing Utilization
Source Author(s)/Editor(s): Toly Chen (Feng Chia University, Taiwan)
DOI: 10.4018/978-1-4666-1870-1.ch003

Keywords: Artificial Intelligence / Computer Science & IT / Information Science Reference

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Abstract

This paper proposes a fuzzy clustering model for fuzzy data with outliers. The model is based on Wasserstein distance between interval valued data, which is generalized to fuzzy data. In addition, Keller’s approach is used to identify outliers and reduce their influences. The authors also define a transformation to change the distance to the Euclidean distance. With the help of this approach, the problem of fuzzy clustering of fuzzy data is reduced to fuzzy clustering of crisp data. In order to show the performance of the proposed clustering algorithm, two simulation experiments are discussed.

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A Fuzzy Clustering Model for Fuzzy Data with Outliers

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