The purpose of this paper is to develop new efficient approaches based on DC programming and DCA to perform clustering via minimum sum-of-squares Euclidean distance. This is a significant improvement over existing methods, enabling faster and more accurate clustering results. The proposed algorithms are designed to handle large datasets and provide robust solutions. By leveraging the strengths of DC programming and DCA, this research aims to revolutionize the field of clustering and provide new insights into the underlying mechanisms.