Unsupervised Kernel-Induced Fuzzy Possibilistic C-Means Technique in Investigating Real-World Data. Issue 1 (1st February 2022)

• Record Type: Journal Article
• Authors: Devi, R
• Is Part Of: Journal of physics. Volume 2199:Issue 1(2022)
• Publication Date: 2022-02-01
• Subjects: Unsupervised clustering -- Typicality -- Fuzzy C-Means -- Membership function; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2199/1/012033
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: The goal of this study is to break down a large dataset into meaningful groupings. Due to the vast dimension and significant resemblance seen among data, exploring divided clusters in real-world datasets is the most difficult assignment. As a result, this work proposes a fuzzy set-based unsupervised effective clustering technique that includes possibilistic memberships, and fuzzy membership degrees into the membership, weighted Cauchy kernel-based similarity measure and center equations. The empirical findings demonstrate the feasibility of the proposed effective clustering technique.