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Neural Processing Letters
Published in: Neural Processing Letters 3/2020

20-09-2020

A New Robust Fuzzy Clustering Approach: DBKIFCM

Authors: Anjana Gosain, Sonika Dahiya

Published in: Neural Processing Letters | Issue 3/2020

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Abstract

A clustering algorithm—Distance Based Gaussian Kernelized Intuitionistic Fuzzy C Means (DBKIFCM) is proposed. This algorithm is based on Gaussian kernel, outlier identification, and intuitionist fuzzy sets. It is intended to resolve the issue of presence of outliers, problem of sensitivity to initialization (STI) and is motivated by good performance of Radial Based Kernelized Intuitionistic Fuzzy C Means (KIFCM-RBF). Experiments are performed on standard 2D data sets such as Diamond (D12 and D15), and Dunn and real-world high dimension data sets such as Fisheriris, Wisconsin breast cancer, and Wine. DBKIFCM outcomes are studied in relation to Fuzzy C Means (FCM), Intuitionistic Fuzzy C Means (IFCM), KIFCM-RBF, Density Oriented Fuzzy C Means (DOFCM). It is observed that proposed approach significantly outperforms the earlier proposed algorithms with respect to outlier identification, effect of noise, issue of STI, and clustering error.
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Appendix
Available only for authorised users
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Metadata
Title
A New Robust Fuzzy Clustering Approach: DBKIFCM
Authors
Anjana Gosain
Sonika Dahiya
Publication date
20-09-2020
Publisher
Springer US
Published in
Neural Processing Letters / Issue 3/2020
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-020-10345-1

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