Fuzzy clustering finding prototypes on classes boundary

Michal Jezewski; Jacek Leski

doi:10.1007/978-3-642-20320-6_19

Fuzzy clustering finding prototypes on classes boundary

Department of Cybernetics, Nanotechnology and Data Processing (RAU/RAU10)

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

In the presented paper, new clustering method based on minimization of a criterion function is proposed. Its goal is to find prototypes placed near the classes boundary. The proposed method may be useful in applications to classification algorithms. The clustering performance was examined using the Ripley dataset, and the results seem to be encouraging.

Original language	English
Pages (from-to)	177-186
Number of pages	10
Journal	Advances in Intelligent and Soft Computing
Volume	95
Issue number	4
DOIs	https://doi.org/10.1007/978-3-642-20320-6_19
Publication status	Published - 1 May 2011

ASJC Scopus subject areas

General Computer Science

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10.1007/978-3-642-20320-6_19

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abstract = "In the presented paper, new clustering method based on minimization of a criterion function is proposed. Its goal is to find prototypes placed near the classes boundary. The proposed method may be useful in applications to classification algorithms. The clustering performance was examined using the Ripley dataset, and the results seem to be encouraging.",

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AB - In the presented paper, new clustering method based on minimization of a criterion function is proposed. Its goal is to find prototypes placed near the classes boundary. The proposed method may be useful in applications to classification algorithms. The clustering performance was examined using the Ripley dataset, and the results seem to be encouraging.

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Fuzzy clustering finding prototypes on classes boundary

Abstract

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