Bi-Level Multi-View fuzzy Clustering with Exponential Distance
Abstract
In this study, we propose extension of fuzzy c-means (FCM) clustering in multi-view environments. First, we introduce an exponential multi-view FCM (E-MVFCM). E-MVFCM is a centralized MVC with consideration to heat-kernel coefficients (H-KC) and weight factors. Secondly, we propose an exponential bi-level multi-view fuzzy c-means clustering (EB-MVFCM). Different to E-MVFCM, EB-MVFCM does automatic computation of feature and weight factors simultaneously. Like E-MVFCM, EB-MVFCM present explicit forms of the H-KC to simplify the generation of the heat-kernel in powers of the proper time during the clustering process. All the features used in this study, including tools and functions of proposed algorithms will be made available at https://www.github.com/KristinaP09/EB-MVFCM.
Keywords
Cite
@article{arxiv.2503.22932,
title = {Bi-Level Multi-View fuzzy Clustering with Exponential Distance},
author = {Kristina P. Sinaga},
journal= {arXiv preprint arXiv:2503.22932},
year = {2025}
}