Ignorance-Based Fuzzy Clustering Algorithm

Author

Jurio, Aranzazu ; Pagola, Miguel ; Paternain, Daniel ; Barrenechea, Edurne ; Sanz, Jose Antonio ; Bustince, Humberto

Author_Institution

Dept. de Autom. y Comput., Public Univ. of Navarra, Pamplona, Spain

fYear

2009

fDate

Nov. 30 2009-Dec. 2 2009

Firstpage

1353

Lastpage

1358

Abstract

In this work an ignorance-based fuzzy clustering algorithm is presented. The algorithm is based on the entropy-based clustering algorithm proposed by Yao et al.. In our proposal, we calculate the total ignorance instead of using the entropy at each data point to select the data point as the first cluster center. The experimental results show that the ignorance-based clustering improves the data classification made by the EFC in image segmentation.

Keywords

fuzzy set theory; image segmentation; pattern clustering; data classification; entropy-based clustering algorithm; ignorance-based fuzzy clustering algorithm; image segmentation; Clustering algorithms; Clustering methods; Data analysis; Entropy; Fuzzy systems; Image segmentation; Intelligent systems; Partitioning algorithms; Proposals; Unsupervised learning; Clustering; Ignorance functions; Image segmentation; Restricted equivalence functions;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems Design and Applications, 2009. ISDA '09. Ninth International Conference on

Conference_Location

Pisa

Print_ISBN

978-1-4244-4735-0

Electronic_ISBN

978-0-7695-3872-3

Type

conf

DOI

10.1109/ISDA.2009.194

Filename

5363909

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2827042