| Summary: | 碩士 === 中原大學 === 應用數學研究所 === 93 === The Similarity-Based Clustering Method (SCM) is applied on Microarray in this thesis. The results demonstrate that SCM has a special function that can dispose of non-cluster gene vectors and still keep the important message in the remaining data. In addition, we combine SCM with two softwares, Michael B. Eisen’s Cluster and Tree-View, so that it can produce the colorful data distribution graph, and can be an easier tool to observe possible clusters for researchers. Overall, we suggest that SCM should be used with Michael B. Eisen’s Cluster and Tree-View to offer better analysis for Microarray data.
Besides, a subprogram is developed in Matlab to facilitate the usage of SCM. Two conditions are compared by this subprogram for the same source data. The first one is the tree graph without SCM while the second one is the tree graph with SCM (the best Gamma value). There are significant differences between the two conditions. The tree graph produced with SCM (the best Gamma value) can help the users recognize the clusters more obviously and easily.
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