A NEW CLUSTERING SCHEME FOR CRISP DATA BASED ON A MEMBERSHIP FUNCTION AND OWA OPERATOR

被引：0

作者：

Basaran, Murat Alper ^{[1
]}

Basaran, Alparslan A. ^{[2
]}

Simonetti, Biagio ^{[3
]}

Lucadamo, Antonio ^{[3
]}

机构：

[1] Akdeniz Univ, Fac Engn Alanya, Dept Engn Management, TR-07425 Alanya, Turkey

[2] Hacettepe Univ, Fac Econ & Adm Sci, Dept Publ Finance, TR-06800 Ankara, Turkey

[3] Univ Sannio, Dept Econ Jurid & Social Syst Studies, I-82100 Benevento, Italy

来源：

HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS | 2012年 / 41卷 / 03期

关键词：

Fuzzy membership function; Fuzzy set; OWA operator; Cluster analysis;

D O I：

暂无

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

Clustering is a very important tool which is applied in several areas, ranging from pattern recognition and marketing to chemistry. A majority of the clustering algorithms classify observations based on distance measures. According to the literature, if the units of measurement of the variables are different, then the result of the clustering is said to be unreliable. Even sometimes, distance based clustering shows contradictory results when measurement units are closely related. Therefore, a new clustering scheme is proposed in this paper based on combining the membership function and OWA operator when classic clustering seems to have failed. For this purpose, a real data set from chemistry with ten variables are used to exemplify the new clustering scheme.

引用

页码：397 / 405

页数：9

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