Genepart algorithm, clustering and feature selection for DNA micro-array data
dc.contributor.advisor | Chairperson, Graduate Committee: Brendan Mumey | en |
dc.contributor.author | Zhang, Weihua | en |
dc.date.accessioned | 2013-06-25T18:43:54Z | |
dc.date.available | 2013-06-25T18:43:54Z | |
dc.date.issued | 2004 | en |
dc.description.abstract | 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. | en |
dc.identifier.uri | https://scholarworks.montana.edu/handle/1/2598 | en |
dc.language.iso | en | en |
dc.publisher | Montana State University - Bozeman, College of Engineering | en |
dc.rights.holder | Copyright 2004 by Weihua Zhang | en |
dc.subject.lcsh | Branch and bound algorithms | en |
dc.subject.lcsh | DNA microarrays | en |
dc.title | Genepart algorithm, clustering and feature selection for DNA micro-array data | en |
dc.type | Thesis | en |
thesis.catalog.ckey | 1149511 | en |
thesis.degree.committeemembers | Members, Graduate Committee: Rockford Ross; Binhai Zhu | en |
thesis.degree.department | Computer Science. | en |
thesis.degree.genre | Thesis | en |
thesis.degree.name | MS | en |
thesis.format.extentfirstpage | 1 | en |
thesis.format.extentlastpage | 85 | en |
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