2000 - Extended fuzzy clustering algorithms

2000 - Extended fuzzy clustering algorithms - ERIM REPORT...

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Unformatted text preview: ERIM REPORT SERIES RESEARCH IN MANAGEMENT ERIM Report Series reference number ERS-2000-51-LIS Publication November 2000 Number of pages 24 Email address first author Kaymak@few.eur.nl Address Erasmus Research Institute of Management (ERIM) Rotterdam School of Management / Faculteit Bedrijfskunde Erasmus Universiteit Rotterdam PoBox 1738 3000 DR Rotterdam, The Netherlands Phone: # 31-(0) 10-408 1182 Fax: # 31-(0) 10-408 9640 Email: info@erim.eur.nl Internet: www.erim.eur.nl Bibliographic data and classifications of all the ERIM reports are also available on the ERIM website: www.erim.eur.nl E XTENDED F UZZY C LUSTERING A LGORITHMS U ZAY K AYMAK AND M AGNE S ETNES E RASMUS R ESEARCH I NSTITUTE OF M ANAGEMENT REPORT SERIES RESEARCH IN MANAGEMENT B IBLIOGRAPHIC DATA AND CLASSIFICATIONS Abstract Fuzzy clustering is a widely applied method for obtaining fuzzy models from data. It has been applied successfully in various fields including finance and marketing. Despite the successful applications, there are a number of issues that must be dealt with in practical applications of fuzzy clustering algorithms. This technical report proposes two extensions to the objective function based fuzzy clustering for dealing with these issues. First, the (point) prototypes are extended to hypervolumes whose size is determined automatically from the data being clustered. These prototypes are shown to be less sensitive to a bias in the distribution of the data. Second, cluster merging by assessing the similarity among the clusters during optimization is introduced. Starting with an over-estimated number of clusters in the data, similar clusters are merged during clustering in order to obtain a suitable partitioning of the data. An adaptive threshold for merging is introduced. The proposed extensions are applied to Gustafson–Kessel and fuzzy c-means algorithms, and the resulting extended algorithms are given. The properties of the new algorithms are illustrated in various examples. 5001-6182 Business 5201-5982 Business Science Library of Congress Classification (LCC) Q 276-280 Mathamatical Statistics M Business Administration and Business Economics M 11 R 4 Production Management Transportation Systems Journal of Economic Literature (JEL) C 8 Data collection and data estimation methodology 85 A Business General 260 K 240 B Logistics Information Systems Management European Business Schools Library Group (EBSLG) 250 B Fuzzy theory Gemeenschappelijke Onderwerpsontsluiting (GOO) 85.00 Bedrijfskunde, Organisatiekunde: algemeen 85.34 85.20 Logistiek management Bestuurlijke informatie, informatieverzorging Classification GOO 31.80 Toepassingen van de wiskunde Bedrijfskunde / Bedrijfseconomie Bedrijfsprocessen, logistiek, management informatiesystemen Keywords GOO Gegevensverwerking, Data mining, Fuzzy theorie, en Algoritmen Free keywords Fuzzy clustering, cluster merging, volume prototypes, similarity. Extended Fuzzy Clustering Algorithms Uzay Kaymak Magne Setnes Erasmus University Rotterdam...
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This note was uploaded on 11/29/2010 for the course DEC 123 taught by Professor Fr during the Spring '10 term at ENS Cachan.

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2000 - Extended fuzzy clustering algorithms - ERIM REPORT...

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