Possibilistic Fuzzy C-means clustering on medical diagnostic systems

Author

Simhachalam, B. ; Ganesan, G.

Author_Institution

Dept. of Eng. Math., GITAM Univ., Visakhapatnam, India

fYear

2014

fDate

27-29 Nov. 2014

Firstpage

1125

Lastpage

1129

Abstract

Classification or Clustering is the task of grouping similar objects based on the similarity among the individuals. The techniques using in clustering are mostly unsupervised methods. In this study, Possibilistic Fuzzy C-means (PFCM) clustering technique is used to classify the patients into different clusters of thyroid diseases. Further, the results of Possibilistic Fuzzy C-means clustering algorithm and Fuzzy c-Means clustering (FCM) algorithm are compared according to the classification performance. The results exhibit that the Possibilistic Fuzzy C-means clustering algorithm performs well.

Keywords

fuzzy set theory; medical diagnostic computing; pattern clustering; unsupervised learning; medical diagnostic systems; patients classification; possibilistic fuzzy c-means clustering; thyroid diseases; unsupervised method; Classification algorithms; Clustering algorithms; Glands; Linear programming; Medical diagnostic imaging; Partitioning algorithms; Prototypes; C-means clustering; Classification; Clustering; Fuzzy objective function; Possibilistic clustering;

fLanguage

English

Publisher

ieee

Conference_Titel

Contemporary Computing and Informatics (IC3I), 2014 International Conference on

Conference_Location

Mysore

Type

conf

DOI

10.1109/IC3I.2014.7019729

Filename

7019729