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Title: An [epsilon]-insensitive approach to fuzzy clustering

Creator:

Łęski, Jacek M.

Date:

2001

Resource Type:

artykuł

Contributor:

Triggiani, Roberto- ed. ; Maksimov, Vyacheslav I. - ed.

Subtitle:

Mathematical Methods of Optimization and Control of Large-Scale Systems

Group publication title:

AMCS, volume 11 (2001)

Abstract:

Fuzzy clustering can be helpful in finding natural vague boundaries in data. The fuzzy c-means method is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greatest disadvantages of this method is its sensitivity to the presence of noise and outliers in the data. The present paper introduces a new [epsilon]-insensitive Fuzzy C-Means ([epsilon]FCM) clustering algorithm. As a special case, this algorithm includes the well-known Fuzzy C-Medians method (FCMED). The performance of the new clustering algorithm is experimentally compared with the Fuzzy C-Means (FCM) method using synthetic data with outliers and heavy-tailed, overlapped groups of the data.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Resource Identifier:

oai:zbc.uz.zgora.pl:58800

Pages:

993-1007

Source:

AMCS, volume 11, number 4 (2001) ; click here to follow the link

Language:

eng

Rights:

Biblioteka Uniwersytetu Zielonogórskiego

Subject and keywords:

fuzzy clustering fuzzy c-means robust methods fuzzy c-medians

Object collections:

Last modified:

Sep 3, 2021

In our library since:

Jul 21, 2021

Number of object content hits:

57

All available object's versions:

https://zbc.uz.zgora.pl/publication/65584

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An [epsilon]-insensitive approach to fuzzy clustering Sep 3, 2021

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