SDCG: A semi-supervised density-based clustering algorithm with granular ball

Abstract

Semi-supervised density-based clustering (SDC) is a density-based clustering technique that integrates both labeled and unlabeled data to improve the accuracy and robustness of cluster assignments. However, many SDC algorithms rely on global parameters to distinguish core from non-core points, which may not be effective for clusters with varying densities or complex shapes. To address this limitation, we propose the Semi-supervised Density-based Clustering algorithm with Granular balls (SDCG), which operates in three phases. First, we construct a set of granular balls to perform an initial segmentation of the data, leveraging the available labeled data, with points within the same granular ball being considered similar. Next, we introduce a strategy that combines coverage and specificity to adjust the granularity of each granule, ensuring that points within the same granule belong to the same cluster. Finally, we employ an adaptive label propagation mechanism based on mutual nearest-neighbor voting, where non-core points are assigned labels according to the highest-voting labels from their mutual nearest neighbors. Overall, SDCG is parameter-free and is able to adaptively perform clustering, improving the performance of the algorithm. Experimental comparisons on both synthetic and real datasets show that our method outperforms existing approaches in terms of efficiency and accuracy, particularly for datasets with varying densities and complex shapes.