 The proposed CSA Diskin Algorithm is a fast and effective way to identify clusters in arbitrary structured datasets. It uses the Chameleon Swarm algorithm to optimize the Diskin Algorithm's parameters, such as the EPS value and noise points, to achieve better clustering results. Additionally, the algorithm uses the deviation in the nearest neighbor search mechanism to identify noise points, solving the problem of over-identifying them. Lastly, the algorithm uses ColorImage Superpixels to improve its performance when segmenting color images. This article was authored by Wei Zhou, Li Min-Wang, Xiuming Han, and others.