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Performance Comparisons Between Unsupervised Clustering Techniques For Microarray Data Analysis On Ovarian Cancer
Published 2006 · Computer Science
In this paper we present some performance comparisons of several unsupervised clustering techniques include: Self-Organizing Map (SOM), Fuzzy C-means (FCM) and hierarchical clustering, and they are employed to analyze the ovarian cancer microarray data. The data includes 15 samples with 9,600 genes and these samples include 5 benign ovarian tumors (OVT), 1 borderline ovarian malignancy (OVTT), 4 ovarian cancers at stage I (OVCAI), and 5 ovarian cancers at stage III (OVCAIII). A regression analysis is used to reduce the dimension and get 9600 residuals of genes. The genes with 100 largest and 100 smallest residual are picked to analyze using analysis of variance (ANOVA). After the ANOVA, 12 gene markers are got and can be used to distinguish OVT, OVTT, OVCAI and OVCAIII samples. The 12 gene markers are performed clustering by the SOM, FCM and hierarchical clustering techniques and to compare the results between these clustering techniques. Our experimental results show that the hierarchical clustering can get best performance of clustering and users do not need to define the number of clusters.