Identification of Cancer Type using Support-Vector Machine based on Fuzzy Clustering

Abstract

By using the clustering results of the Fuzzy c-means (FCM) method for cancer gene data, the discrimination results obtained by applying Support-Vector Machine (SVM) to each cluster, and the discrimination results based on the clustering results of the K-means method are compared. As a result, it was suggested that the results obtained by the FCM tended to have less variation in discrimination performance among clusters than the results following the K-means method. Originally, it was said that the types of cancer, based on gene expression data, can reflect more actual data if treated as a complex group with various labels of the types of cancer. The performance evaluation of this study shows that the extraction of a fuzzy cluster with multiple properties applies to identifying the types of cancer from this type of data.