Unsupervised Kernel-Induced Fuzzy Possibilistic C-Means Technique in Investigating Real-World Data

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.

Performance Comparison of Dimensional Reduction using Principal Component Analysis with Alternating Least Squares in Modified Fuzzy Possibilistic C-Means and Fuzzy Possibilistic C-Means

“…FPCM is also used to investigate data in the real world, namely to break down extensive data sets into meaningful clusters. FPCM can cluster data in the database effectively [12]. In the health sector, FPCM is used in knee osteoarthritis analysis with kernel functions to handle the problem of data that cannot be separated.…”