Density-based clustering method with adaptive neighbors

Graph-based clustering methods partition data by leveraging neighborhood relationships among data points. These methods are often sensitive to the predefined affinity matrix. To achieve adaptive and optimal clustering, recent studies have introduced techniques that learn adaptive neighbors automatically. However, these methods still depend on an essential parameter: the number of neighbors, which must be specified in advance. To address this limitation, this paper proposes a novel objective function that incorporates a density measure into a self-weighted adaptive neighbor clustering framework. Extensive analysis and experimental results show that the proposed approach exhibits reduced sensitivity to the number of neighbors and achieves superior performance compared to several established clustering algorithms. Furthermore, it preserves the fundamental advantages of self-weighted adaptive neighbor clustering.