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
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;
Conference_Titel :
Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1507-4
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZ-IEEE.2012.6251144
