The density peaks clustering (DPC) algorithm achieves clustering by identifying cluster centers based on local density and relative distance. However, it tends to overlook cluster centers in low-density regions for data with uneven density distribution and unbalanced cluster sizes. Therefore, the number of clusters needs to be set artificially. Besides, if a data point allocation occurs to be wrong in the whole strategy, it will lead to incorrect allocation of subsequent points. To address these issues, this study proposes an adaptive sparse-aware density peaks clustering algorithm. Firstly, fuzzy points are introduced to minimize their impact on the subcluster merging process. Secondly, the subtractive clustering method is used to identify the low-density regions’ center. Then, noise is identified and subcluster centers are updated based on new local density and reverse nearest neighbor. Finally, a redefined global overlap metric combined with global separability guides subcluster merging while automatically determining clustering results using these metrics. Experimental results demonstrate that compared to DPC and its improved algorithms, the proposed algorithm effectively identifies sparse clusters in both synthetic and UCI datasets while reducing chain reactions caused by non-center assignments. Also, the proposed algorithm can automatically determine the optimal clustering number, ultimately yielding more accurate clustering results.