A main objective of this work is to present research on automatic detection of skin changes. In the paper, we use unsupervised learning methods to assess the asymmetry of the lesions for dermatology as tool for computer-aided diagnostic systems. The asymmetry features are extracted from the images as vectors and then calculated using two DASMShape methods. The clusterization methods usually provided us with some mean values or center points, of the centroids. For that points we have calculated the asymmetry measures. In the next step, to evaluate the clusters and their clusters to classes assignment, we have used classification methods: Support Vector Machines (SVM), k nearest neighbours (kNN), Random Forrest, Multilayer Perceptron, and C4.5 to assess defined above dermatological asymmetry procedure. In the research, we have tested several clusterization methods and chosen three of them: Canopy, k-Means (KM), and Expectation-Maximization (EM).