Clustering Microarray Data
نویسنده
چکیده
Using microarray data, which gives thousands of genes’ expression levels at once, we examine different clustering techniques and evaluation methods to effectively cluster the noisy data. The results of this study has implications for the field of biology. Genes with similar functions are grouped together, which gives insight into specific genes and their role in the cell. The cluster analysis employed uses different distance metrics, including Euclidean, Pearson’s correlation, and percentage bend correlation, and we use the cluster methods of hierarchical, Partitioning Around Medoids (PAM), and Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH). The evaluation methods involve silhouette width, the L method, and the bootstrap. We conclude with an experiment in improving the sample and the clustering output using the bootstrap method.
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