DocumentCode
2752149
Title
A novel adaptive fuzzy c-means algorithm for interval data type
Author
De Souza, Renata M C R ; De Carvalho, Leonardo Vieira ; Júnior, Nicomedes L Cavalcanti
Author_Institution
Centro de Inf., Cidade Univ., Recife, Brazil
fYear
2012
fDate
10-15 June 2012
Firstpage
1
Lastpage
5
Abstract
A novel extension of the fuzzy c-means clustering algorithm for interval data type based on an adaptive Euclidean distance is presented. The proposed method furnishes a fuzzy partition and a prototype for each cluster by optimizing a criterion based on an adaptive Euclidean distance that changes at each algorithm iteration. Experiments with real and synthetic data sets show the usefulness of this method.
Keywords
fuzzy set theory; iterative methods; pattern clustering; adaptive Euclidean distance; fuzzy partition; interval data type; iteration algorithm; novel adaptive fuzzy c-means algorithm; Clustering algorithms; Clustering methods; Euclidean distance; Indexes; Partitioning algorithms; Prototypes; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on
Conference_Location
Brisbane, QLD
ISSN
1098-7584
Print_ISBN
978-1-4673-1507-4
Electronic_ISBN
1098-7584
Type
conf
DOI
10.1109/FUZZ-IEEE.2012.6251144
Filename
6251144
Link To Document