Representative Point-Based Clustering With Neighborhood Information for Complex Data Structures

Discovering clusters remains challenging when dealing with complex data structures, including those with varying densities, arbitrary shapes, weak separability, or the presence of noise. In this article, we propose a novel clustering algorithm called representative point-based clustering with neighborhood information (RPC-NI), which highlights the significance of neighborhood information often overlooked by existing clustering methods. The proposed algorithm first introduces a new local centrality metric that integrates both neighborhood density and topological convergence to identify core representative points, effectively capturing the structural characteristics of the data. Subsequently, a density-adaptive distance is defined to evaluate dissimilarities between these core representative points, and such distance is used to construct a minimum spanning tree (MST) over these points. Finally, an MST-based clustering algorithm is employed to yield the desired clusters. Incorporating neighborhood information enables RPC-NI to comprehensively determine representative points, and having multiple representative points per cluster allows RPC-NI to adapt to clusters of arbitrary shapes, varying densities, and different sizes. Extensive experiments on widely used datasets demonstrate that RPC-NI outperforms baseline algorithms in terms of clustering accuracy and robustness. These results provide further evidence for the importance of incorporating neighborhood information discovering clusters with complex structures.