Comparison of cancer classification algorithms based on clustering analysis

Nowadays, omics datasets have been widely used to study cancer and other related problems, but there are many cancer subtypes in some types of cancer, and some types have not been studied, so we must use unsupervised methods for cluster analysis. Cluster analysis is the process of finding similar data points in a pile of data points and classifying them. In this paper, five omics data sets are used to compare the three clustering methods, in order to find a more suitable clustering method for omics datasets. The conclusion of this paper is that OPTICS method is a better clustering method.