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Neural Computing and Applications

Erschienen in:

16.07.2016 | Original Article

Fuzzy least squares twin support vector clustering

verfasst von: Reshma Khemchandani, Aman Pal, Suresh Chandra

Erschienen in: Neural Computing and Applications | Ausgabe 2/2018

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Abstract

In this paper, we have formulated a fuzzy least squares version of recently proposed clustering method, namely twin support vector clustering (TWSVC). Here, a fuzzy membership value of each data pattern to different cluster is optimized and is further used for assigning each data pattern to one or other cluster. The formulation leads to finding k cluster center planes by solving modified primal problem of TWSVC, instead of the dual problem usually solved. We show that the solution of the proposed algorithm reduces to solving a series of system of linear equations as opposed to solving series of quadratic programming problems along with system of linear equations as in TWSVC. The experimental results on several publicly available datasets show that the proposed fuzzy least squares twin support vector clustering (F-LS-TWSVC) achieves comparable clustering accuracy to that of TWSVC with comparatively lesser computational time. Further, we have given an application of F-LS-TWSVC for segmentation of color images.

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Titel: Fuzzy least squares twin support vector clustering
verfasst von: Reshma Khemchandani
Aman Pal
Suresh Chandra
Publikationsdatum: 16.07.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 2/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2468-4

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