In this work, a novel soft subspace fuzzy clustering algorithm MKEWFC-K is proposed by extending the existing entropy weight soft subspace clustering algorithm ...
Under the classical fuzzy clustering framework, it groups data objects in the entire data space but assign different weights to different dimensions of clusters.
By incorporating multiple-kernel learning strategy into the framework ofsoft subspace fuzzy clustering, MKEWFC-K can learning the distance function ...
In this work, a novel soft subspace fuzzy clustering algorithm MKEWFC-K is proposed by extending the existing entropy weight soft subspace clustering algorithm ...
Aug 3, 2024 · This paper proposes a novel clustering ensemble approach for improving the robustness and accuracy of the MKFC algorithm.
In this study, we present a comprehensive comparative analysis of kernel-based fuzzy clustering and fuzzy clustering. Kernel based clustering has emerged as ...
Jul 3, 2024 · In this study, we present a comprehensive comparative analysis of kernel-based fuzzy clustering and fuzzy clustering.
We propose the Knowledge-induced Multiple Kernel Fuzzy Clustering (KMKFC) algorithm. First, to extract knowledge points better, the Relative Density-based ...
Feb 1, 2021 · In this paper, we propose a robust multiple kernel subspace clustering based on low rank consensus kernel learning (MKLRSC) method for data clustering.
Missing: soft | Show results with:soft
A multiple kernel clustering (MKC) algorithm that simultaneously finds the maximum margin hyperplane, the best cluster labeling, and the optimal kernel is ...
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