Genepart algorithm, clustering and feature selection for DNA micro-array data

dc.contributor.advisorChairperson, Graduate Committee: Brendan Mumeyen
dc.contributor.authorZhang, Weihuaen
dc.date.accessioned2013-06-25T18:43:54Z
dc.date.available2013-06-25T18:43:54Z
dc.date.issued2004en
dc.description.abstractThis 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.urihttps://scholarworks.montana.edu/handle/1/2598en
dc.language.isoenen
dc.publisherMontana State University - Bozeman, College of Engineeringen
dc.rights.holderCopyright 2004 by Weihua Zhangen
dc.subject.lcshBranch and bound algorithmsen
dc.subject.lcshDNA microarraysen
dc.titleGenepart algorithm, clustering and feature selection for DNA micro-array dataen
dc.typeThesisen
thesis.catalog.ckey1149511en
thesis.degree.committeemembersMembers, Graduate Committee: Rockford Ross; Binhai Zhuen
thesis.degree.departmentComputer Science.en
thesis.degree.genreThesisen
thesis.degree.nameMSen
thesis.format.extentfirstpage1en
thesis.format.extentlastpage85en

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