HFMSTMC: Improved Binary Grey Wolf Optimization in Hybrid Fuzzy Minimum Spanning Tree Clustering with Manifold Learning

Abstract

Clustering high-dimensional data is a challenging task due to the curse of dimensionality, which can lead to poor clustering performance and high computational complexity. Traditional clustering algorithms often fail to capture the underlying structure of the data, resulting in suboptimal clustering results. Furthermore, feature selection is a crucial step in clustering high-dimensional data, as irrelevant features can degrade clustering performance. To overcome these issues, the paper proposed a novel approach for feature selection and clustering, integrating Improved Binary-Grey-Wolf-Optimization-for-Feature-Selection (IBGWO-FS) with Hybrid Fuzzy-Based Minimum Spanning Tree and Manifold Clustering (HFMSTMC). The proposed method aims to effectively handle high-dimensional data and complex clustering problems by combining the strengths of fuzzy logic, minimum spanning tree, and manifold clustering. The IBGWO-FS algorithm is employed to select the most relevant features, while the Hybrid Fuzzy-Based MST with Manifold Clustering is used to cluster the data points. Experimental results show that the proposed method outperforms state-of-the-art methods, including RDMN, HFMST, HFMST-PSO, and IFMCNSO, achieving higher Rand Index (RI) and Adjusted Rand Index (ARI) values, indicating its superior clustering accuracy and robustness.