Possibilistic Fuzzy C-means clustering on medical diagnostic systems

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 differe...

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Veröffentlicht in:2014 International Conference on Contemporary Computing and Informatics (IC3I) S. 1125 - 1129
Hauptverfasser: Simhachalam, B., Ganesan, G.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.11.2014
Schlagworte:
C-means clustering
Classification
Classification algorithms
Clustering
Clustering algorithms
Fuzzy objective function
Glands
Linear programming
Medical diagnostic imaging
Partitioning algorithms
Possibilistic clustering
Prototypes
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Zusammenfassung: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.
DOI:10.1109/IC3I.2014.7019729