Initializing Partition-Optimization Algorithms Maitra, Ranjan
dc.contributor.department Statistics 2018-02-17T18:39:34.000 2020-07-02T06:58:05Z 2020-07-02T06:58:05Z Thu Jan 01 00:00:00 UTC 2009 2009-01-01
dc.description.abstract <p>Clustering data sets is a challenging problem needed in a wide array of applications. Partition-optimization approaches, such as k-means or expectation-maximization (EM) algorithms, are suboptimal and find solutions in the vicinity of their initialization. This paper proposes a staged approach to specifying initial values by finding a large number of local modes and then obtaining representatives from the most separated ones. Results on test experiments are excellent. We also provide a detailed comparative assessment of the suggested algorithm with many commonly used initialization approaches in the literature. Finally, the methodology is applied to two data sets on diurnal microarray gene expressions and industrial releases of mercury.</p>
dc.description.comments <p>This is a manuscript of an article from <em>IEEE/ACM Transactions on Computational Biology and Bioinformatics</em> 6 (2009): 144, doi: <a href="" target="_blank">10.1109/TCBB.2007.70244</a>. Posted with permission. Copyright 2009 IEEE.</p>
dc.format.mimetype application/pdf
dc.identifier archive/
dc.identifier.articleid 1080
dc.identifier.contextkey 8832042
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/80
dc.language.iso en
dc.source.bitstream archive/|||Sat Jan 15 02:04:43 UTC 2022
dc.source.uri 10.1109/TCBB.2007.70244
dc.subject.disciplines Statistics and Probability
dc.subject.keywords —Toxic Release Inventory
dc.subject.keywords methylmercury
dc.subject.keywords multi-Gaussian mixtures
dc.subject.keywords protein localization
dc.subject.keywords singular value decomposition
dc.title Initializing Partition-Optimization Algorithms
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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