**4. Experimental Study**

In this section, MDSVC is compared with k-means (KM) [4], optimal margin distribution clustering (ODMC) [22], spectral clustering (SC) [23], mean shift (MS) [24], and hierarchical clustering (HC) [25]. We adopt the results of K-means acting as a baseline rather than maximum margin clustering (MMC) [20] since it could not return results in a reasonable time for most datasets. We experimentally evaluate the performance of our MDSVC compared with the original algorithms of SVC on classic artificial datasets and several medium-sized datasets; that is, we focus on the difference between MDSVC and SVC. Table 2 summarizes the statistics of these data sets. All real-world datasets used for our experiments can be found at UCI (http://archive.ics.uci.edu/ml, 2 February 2021). In Table 2, all of the samples of artificial datasets, namely convex, dbmoon, and ring, are added with Gaussian noises, which are representative of different types of datasets. All algorithms are implemented with MATLAB R2021a on a PC with a 2.50 GHz CPU and 64 GB memory.

**Table 2.** Experimental Datasets.

