Abstract: Several typical supervised clustering methods, Gaussian mixture model-based supervised clustering (GMM), K-Nearest-Neighbor (KNN), binary support vector machines (SVMs) and multicategory support vector machines (MC-SVMs), were employed to classify the computer simulation data, yeast cell cycle microarray data and 60 human cancer cell lines (NCI-60) microarray data. False positive, false negative, true positive, true negative and clustering accuracy were compared among these methods. The results are as follows. (1) For classify thousands of gene expression data, the performances of two GMM methods have the maximal clustering accuracy and the least overall FP+FN error numbers based on the assumption that the whole set of microarray data is a finite mixture of multivariate Gaussian distributions. Furthermore, when the number of training sample is very small, the clustering accuracy of GMMⅡ method have superiority over GMMⅠ method. (2) In the general, the superior classification performance of the MC-SVMs are more robust and more practical, which are less sensitive to the curse of dimensionality and not only next to GMM method in clustering accuracy to thousands of gene expression data, but also more robustness to a small number of high-dimensional gene expression samples than other techniques. (3)Among MC-SVMs, in case of large sample sizes, OVO and DAGSVM perform better; In case of moderate sample sizes, five MC-SVMs methods perform very similar; Otherwise, OVR, WW and CS yield the better results when sample sizes are small. (4) A suggestion for the supervised clustering microarray data is that one should consider the data feature and experiment when choose an appropriate method. Two kinds of these methods should be trial calculation to obtain better clustering result.

Key words: Microarray, Supervised Clustering, K-Nearest-Neighbor, Support Vector Machines