Research Article
Graph Theory Approach Probability Distribution Based Intuitionistic Fuzzy C-means Clustering Using LINEX Hellinger Distance and Its Applications
Issue:
Volume 12, Issue 2, June 2026
Pages:
20-31
Received:
21 April 2026
Accepted:
8 May 2026
Published:
12 November 2026
Abstract: Clustering is an essential task to unsupervised learning, its used to partition a dataset into groups of similar elements. In addition to traditional clustering methods, intuitionistic fuzzy c-means (IFCM) clustering algorithm has been studied to capturing uncertain information in the dataset. However, the IFCM algorithm has limitations such as the random initialization of cluster centers and the unrestricted influence of all samples on all cluster centers. Therefore, a robust algorithm named probability distribution of the minimal spanning tree based intuitionistic fuzzy c-means clustering algorithm using LINEX Hellinger distance is proposed for improving shortcomings of the IFCM. Firstly, the probability distribution of the minimal spanning tree algorithm organizes the initial cluster center based on the contribution of local density information from the data. Secondly, MST node probability distribution based IFCM using Hellinger distance, the membership degree boundary is assigned for the data samples on all cluster centers in the clustering process. Finally, the performance of the proposed modified IFCM algorithm is numerically validated using random dataset, partition coefficient and validation function are used to evaluate the validity of clustering also this paper compares the results of proposed method with the results of existing basic intuitionistic fuzzy c-means.
Abstract: Clustering is an essential task to unsupervised learning, its used to partition a dataset into groups of similar elements. In addition to traditional clustering methods, intuitionistic fuzzy c-means (IFCM) clustering algorithm has been studied to capturing uncertain information in the dataset. However, the IFCM algorithm has limitations such as the...
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,
Nithya
