Competitive algorithms for the clustering of noisy data

CCUR > College of Natural Science and Engineering > College of Engineering > Department of Computer Science and Information Engineering > journal articles > Item 987654321/2474

Please use this identifier to cite or link to this item: https://irlib.pccu.edu.tw/handle/987654321/2474

Title:	Competitive algorithms for the clustering of noisy data
Authors:	Yang TN, Wang SD
Contributors:	資訊科學系
Date:	2004
Issue Date:	2009-11-02 11:29:04 (UTC+8)
Abstract:	In this paper, we consider the issue of clustering when outliers exist. The outlier set is defined as the complement of the data set. Following this concept, a specially designed fuzzy membership weighted objective function is proposed and the corresponding optimal membership is derived. Unlike the membership of fuzzy c-means, the derived fuzzy membership does not reduce with the increase of the cluster number. With the suitable redefinition of the distance metric, we demonstrate that the objective function could be used to extract c spherical shells. A hard clustering algorithm alleviating the prototype under-utilization problem is also derived. Artificially generated data are used for comparisons. (C) 2002 Elsevier B.V. All rights reserved.
Relation:	FUZZY SETS AND SYSTEMS v.141 n.2 Pages: 281-299
Appears in Collections:	[Department of Computer Science and Information Engineering] journal articles

Files in This Item:

File	Description	Size	Format
index.html		0Kb	Text	888	View/Open