| Modern DNA microarray technology provides means of measuring gene expression patterns of the whole genome of simple organisms at once. Exploratory analysis of these large-scale expression datasets is becoming vital to extracting functional information from the measurements. Many clustering algorithms have been used to analyze the microarray gene expression data. In this dissertation, I review various clustering algorithms for the purpose of empirical demonstration of the application of algorithms. We provide guidelines on how to select suitable clustering algorithms and raise relevant issues in the extraction of meaningful biological information from microarray expression data. |
