High-dimensional data analysis can be challenging due to the size and complexity of the data. A new clustering algorithm was developed to improve the quality of clustering in high-dimensional data. By combining distance optimization and density methods, the algorithm determines initial clustering centers more effectively. Simulation experiments showed that this improved algorithm produces better clustering results compared to traditional methods.