STUDY AND IMPLEMENTATION OF CLUSTERING TECHNIQUE FOR DATA GENETICS OF MICROARRAY. - Lumbantoruan -NIM : 13505605, Rosni
Abstract: Furthermore, the existing of microarray technology, that generates thousands of data in a single experiment. For large amounts of gene expression data have been generated by microarray technology so there is a need to analyze data. One of technique to analyze microarray data is clustering. In this final project had been built softwares module to do clustering microarray data. The implemented algorithms are Hierarchical Clustering (HC), K-Means, and Self Organizing Maps (SOM). Many clustering algorithms have been proposed to analyze gene expression data, but little guidance is available to assess the predictive power of the algorithms. The built softwares module implement Figure Of Merit, a solution to the addressed problem. Figure Of Merit assesses the predictive power of clustering algorithm. The output of Figure Of Merit is then visualized in form of graphics. To simplify the representation, data cluster produced by Hierarchical Clustering algorithm is visualized in the form of tree. Visualization use the existing softwares module named TreeView. The softwares module is a desktop application built in C++.