(Montana State University - Bozeman, College of Engineering, 2004) Zhang, Weihua; Chairperson, Graduate Committee: Brendan Mumey
This paper provides the theoretical analysis of a new clustering and feature selection algorithm for the DNA micro-array data. This algorithm utilizes a branch and bound algorithm as the basic tool to quickly generate the optimal tissue sample partitions and select the gene subset which contributes the most to certain sample partition, it also combines the statistical probability method to identify important genes that have meaningful biological relationships to the classification or clustering problem. The proposed method combines feature selection and clustering processes and can be applied to the diagnostic system. Simulation results and analysis shown in the paper support the effectiveness of the combined algorithm.
