Title :
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
fDate :
Nov. 30 2009-Dec. 2 2009
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;
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
DOI :
10.1109/ISDA.2009.194
