Comparative Performance Analysis of Ellipsoidal Support Vector Clustering on Biomedical Data Sets

The performance of clustering algorithms is very important in biomedical research because they help in the pre-diagnosis of diseases, recognize diseases and take necessary precautions in diseased people. However, most clustering algorithms use the Euclidean distance as a similarity metric. Euclidean distance assumes the variances of the data samples are equal. The performance of traditional clustering methods that use Euclidean distance is quite low if the data contains noise or outlier samples. This study proposes the Ellipsoidal Support Vector Clustering algorithm, which is one of the kernel-based clustering methods, in order to eliminate the above mentioned problems. In the ESVC algorithm, there is no need to specify the cluster number in advance. Moreover, the ESVC algorithm is capable of generating clustering shapes that are appropriate to the distribution of data using the mahalanobis similarity metric. The proposed ESVC algorithm was applied to both real biomedical data and synthetic data and then compared to conventional clustering methods. It has been observed that ESVC algorithm performs well in terms of accuracy, specificity and sensitivity