Title :
Possibilistic approach to clustering of interval data
Author :
Pimentel, Bruno Almeida ; de Souza, Renata M. C. R.
Author_Institution :
Centro de Inf., UFPE, Recife, Brazil
Abstract :
Clustering analysis is an important tool used in several application domains like pattern recognition, computer vision and computational biology to summarize data. The fuzzy c-means method (FCM) is the most popular fuzzy clustering algorithm, however this method is sensitive to noisy data. The possibilistic c-means (PCM) was created as an alternative to solve this problem. The propose in this work is extend the classical PCM to symbolic interval-valued data. Experiments with artificial and real symbolic interval-type data sets are performed and the results show the superiority of PCM in relation to FCM methods to interval-valued data.
Keywords :
fuzzy set theory; pattern clustering; FCM methods; artificial symbolic interval-type data sets; clustering analysis; computational biology; computer vision; fuzzy c-mean method; fuzzy clustering algorithm; interval data clustering; noisy data; pattern recognition; possibilistic approach; possibilistic c-mean method; real symbolic interval-type data sets; Clustering algorithms; Clustering methods; Equations; Muscles; Phase change materials; Prototypes; Vectors; Fuzzy C-Means; Interval data; Noisy data; Pattern recognition; Possibilistic C-Means;
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-1-4673-1713-9
Electronic_ISBN :
978-1-4673-1712-2
DOI :
10.1109/ICSMC.2012.6377698
