ICTACT Journal on Soft Computing

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An Application of PSO-Based Intuitionistic Fuzzy Clustering to Medical Datasets


Affiliations
1 School of Computer Technology and Applications, Kongu Engineering College, India
2 Department of Computer Science, NKR Government Arts College for Women, India
     

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Clustering is the process of splitting data into several groups based on the characteristics of data. Fuzzy clustering assigns a data object to various clusters based on different membership values. In medical field, the diagnosis of the disease has to be done without faults and in an earlier time without any delay. So, there is a need to represent imprecise nature of the data. To represent vague data in a clear manner, Intuitionistic fuzzy set introduces a parameter called hesitancy degree. In case of Intuitionistic fuzzy clustering, this indicates that the user is not aware whether the object belongs to or not belongs to a cluster. In such a case, hesitancy can very well represent the inherent noise in the data or the ignorance of the user that is given by the state ‘may be’. All clustering algorithms choose the initial seed in a random fashion. But, this creates a serious impact on the convergence of the algorithm and the clustering algorithms tend to fall into local minima. This work utilizes Intuitionistic fuzzy Particle Swarm Optimization to initialize the centroids for the Intuitionistic fuzzy clustering algorithm. The algorithm is executed over medical datasets from UCI repository and the results indicate that optimal clusters are achieved. The proposed method performs well when compared with IFCM and FCM-PSO.

Keywords

Clustering, Intuitionistic Fuzzy Set, Particle Swarm Optimization, Inertia Weight, Lambda Value.
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  • An Application of PSO-Based Intuitionistic Fuzzy Clustering to Medical Datasets

Abstract Views: 437  |  PDF Views: 4

Authors

S. Parvathavarthini
School of Computer Technology and Applications, Kongu Engineering College, India
N. Karthikeyani Visalakshi
Department of Computer Science, NKR Government Arts College for Women, India
S. Shanthi
School of Computer Technology and Applications, Kongu Engineering College, India
K. Lakshmi
School of Computer Technology and Applications, Kongu Engineering College, India

Abstract


Clustering is the process of splitting data into several groups based on the characteristics of data. Fuzzy clustering assigns a data object to various clusters based on different membership values. In medical field, the diagnosis of the disease has to be done without faults and in an earlier time without any delay. So, there is a need to represent imprecise nature of the data. To represent vague data in a clear manner, Intuitionistic fuzzy set introduces a parameter called hesitancy degree. In case of Intuitionistic fuzzy clustering, this indicates that the user is not aware whether the object belongs to or not belongs to a cluster. In such a case, hesitancy can very well represent the inherent noise in the data or the ignorance of the user that is given by the state ‘may be’. All clustering algorithms choose the initial seed in a random fashion. But, this creates a serious impact on the convergence of the algorithm and the clustering algorithms tend to fall into local minima. This work utilizes Intuitionistic fuzzy Particle Swarm Optimization to initialize the centroids for the Intuitionistic fuzzy clustering algorithm. The algorithm is executed over medical datasets from UCI repository and the results indicate that optimal clusters are achieved. The proposed method performs well when compared with IFCM and FCM-PSO.

Keywords


Clustering, Intuitionistic Fuzzy Set, Particle Swarm Optimization, Inertia Weight, Lambda Value.

References