Comparing K-Mean Clustering Methods of DNA in Brain Tumors for High-Dimensional Data
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Abstract
This study aims to compare the performance of clustering DNA of brain tumor patients of k-means three methods, namely the Hartigan-Wong, Forgy, and MacQueen methods. The independent variables are DNA as 989 genes, and the dependent variable is the level of a brain tumor in 43 patients. In this case, the number of the independent variable is larger than the number of patients or called the high-dimensional data. The experiment is conducted by random DNA samples of 200, 400, 600, and 800 genes and fixed 5, 10, 15, 20, 25, and 30 groups by 1,000 replications. Comparing clustering performance is the mean data differences between the groups' criteria. The results of k-means clustering methods find that the Hartigan-Wong method has the best performance for all situations. However, the Hartigan-Wong method shows the most significant difference in data between groups compared to Forgy and MacQueen methods. The number of independent variables has not affected clustering performance.
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