Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Supervised fuzzy clustering for the identification of fuzzy classifiers
Pattern Recognition Letters
Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm
Neural Processing Letters
Journal of the American Society for Information Science and Technology
Computers in Biology and Medicine
Implementing automated diagnostic systems for breast cancer detection
Expert Systems with Applications: An International Journal
Breast cancer diagnosis using least square support vector machine
Digital Signal Processing
Expert Systems with Applications: An International Journal
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In recent years the use of fuzzy clustering techniques in medical diagnosis is increasing steadily, because of the effectiveness of fuzzy clustering techniques in recognizing the systems in the medical database to help medical experts in diagnosing diseases. This study focuses on clustering lung cancer dataset into three types of cancers which are leading cause of cancer death in the world. This paper invents effective fuzzy clustering techniques by incorporating hyper tangent kernel function, and entropy methods for analyzing the Lung Cancer database to assist physician in diagnosing lung cancer. Further this paper proposes an algorithm to initialize the cluster centers to speed up the process of the algorithms. The effectiveness of the proposed methods has been proved through the experimental works on synthetic dataset, Wine dataset and IRIS dataset in terms of running time, number of iterations, visual segmentation effects and clustering accuracy. And then this paper proposes the proposed method on Lung cancer database to divide it into three types of lung cancers. In addition this paper proves the superiority of the proposed methods by comparing the obtained classes with reference classes through Error Matrix.