Clustering Techniques used for Gene Cancers

Abstract

Melanoma is the deadliest skin cancer. It can be developed in any parts of the human body. The cancer disease can be cured if it is diagnosed early and proper treatment is taken. In cancer classification, there is a problem in handling the large data of cancer. Large data contains meaningless data and redundant data. Therefore, to overcome the problem, many computer approaches for classification have been proposed in the previous literature. This time, the clustering process for melanoma is conducted using Gaussian Mixture Models, KMeans_rcpp, K-Means and Support Vector Machine. Therefore, the purpose of this research is to identify and evaluate the performance of the accuracy of genes that contain melanoma skin cancer using the clustering algorithms.