Discovering generalized clusters with adaptive mixture density-based clustering

Density-based clustering algorithms are widely used for their ability to handle complex datasets; however, their performance often depends on the definition of density and is sensitive to data shapes, density variations, and noise. To address these challenges, we propose a novel parameter-free clustering algorithm named "Bombing", which can automatically determine the number of clusters. The algorithm enhances adaptability to complex data structures by combining global and local density estimations and utilizing dynamically adjusted density measures. By introducing the concept of generalized clusters and employing a "bombing" process that propagates clustering outward from core points, the method effectively mitigates the impact of cluster shapes, overlaps, and noise. Additionally, through the analysis of adjacency degrees, the algorithm can automatically detect the optimal number of clusters without prior knowledge. Experimental results on various synthetic and real datasets show that our proposed algorithm outperforms existing methods in clustering accuracy and robustness. It effectively handles noise and cluster overlap, and excels in identifying the optimal value of K .