Data mining is the process used to analyze a large quantity of heterogeneous data to extract useful information. Meanwhile, many data mining techniques are used; clustering classified to be an important technique, used to divide data into several groups called, clusters. Those clusters contain, objects that are homogeneous in one cluster, and different from other clusters. As a reason of the dependence of many applications on clustering techniques, while there is no combined method for clustering; this study compares k-mean, Fuzzy c-mean, self-organizing map (SOM), and support vector clustering (SVC); to show how those algorithms solve clustering problems, and then; compares the new methods of clustering (SVC) with the traditional clustering methods (K-mean, fuzzy c-mean and SOM). The main findings show that SVC is better than the k-mean, fuzzy c-mean and SOM, because; it doesn’t depend on either number or shape of clusters, and it dealing with outlier and overlapping. Finally; this paper show that; the enhancement using the gradient decent, and the proximity graph, improves the support vector clustering time by decreasing its computational complexity to O(nlogn) instead of O(n2d), where; the practical total time for improvement support vector clustering (iSVC) labeling method is better than the other methods that improve SVC.