In this chapter, we present the results of the numerical experiments. We begin by highlighting the importance of the procedure for finding starting cluster centers in incremental clustering algorithms. Next, we provide a detailed analysis of the performance of optimization-based clustering algorithms as well as some heuristic approaches. In addition, we compare the numerical outcomes of clustering algorithms with various similarity measures. Finally, we report results for finding compact and well-separated clusters and robust clustering. To assess and compare the effectiveness of these algorithms, we employ evaluation metrics such as cluster validity indices, purity, and performance profiles.