A Fiedler’s Approach to LINEX Intuitionistic Fuzzy C-means Clustering Induced Spectral Initialization for Data Analysis
Mrs.S. Nithya, K. Bhuvaneswari, S. Senthil · 2023
Clustering is a common technique for statistical data analysis. The clustering method based on intuitionistic fuzzy set has attracted more and more scholar’s attention nowadays. This paper discusses the intuitionistic fuzzy C-means clustering algorithm. There are a number of clustering techniques developed in the past using different distance/similarity measure. In researchers have used various distance measure like Hamming distance, Euclidean distance etc., to solve the clustering problems. In this paper, we proposed a novel LINEX for intuitionistic fuzzy c means clustering based on minimal spanning tree using Fiedler’s approach initialization method. Our main motives of using the LINEX methods consist inducing a class of robust non-Euclidean distance measures for the original data space to derive new objective functions and thus clustering the integration of datasets, enhancing robustness of the original clustering algorithms to noise and outliers, and still retaining computational simplicity. The proposed Fiedler’s approach LINEX IFCM, which requires the determination of the eigenvector belonging to the second Eigen value of the Laplacian matrix. Finally, evaluation is illustrated by the intuitionistic fuzzy C-means clustering method and the method is compared with the fuzzy C-means clustering method as well.