The New and Computationally Efficient MIL-SOM Algorithm: Potential Benefits for Visualization and Analysis of a Large-Scale High-Dimensional Clinically Acquired Geographic Data

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dc.contributor.authorOyana, Tonny J.en
dc.contributor.authorAchenie, Luke E. K.en
dc.contributor.authorHeo, Joonen
dc.contributor.departmentChemical Engineeringen
dc.date.accessed2014-06-11en
dc.date.accessioned2014-06-12T13:28:56Zen
dc.date.available2014-06-12T13:28:56Zen
dc.date.issued2012en
dc.description.abstractThe objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen's SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this improvement translates to faster convergence. The basic idea is primarily motivated by the urgent need to develop algorithms with the competence to converge faster and more efficiently than conventional techniques. The MIL-SOM algorithm is tested on four training geographic datasets representing biomedical and disease informatics application domains. Experimental results show that the MIL-SOM algorithm provides a competitive, better updating procedure and performance, good robustness, and it runs faster than Kohonen's SOM.en
dc.description.sponsorshipResponsive and Reflective University Initiative (RRUI), Southern Illinois Unversityen
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationTonny J. Oyana, Luke E. K. Achenie, and Joon Heo, "The New and Computationally Efficient MIL-SOM Algorithm: Potential Benefits for Visualization and Analysis of a Large-Scale High-Dimensional Clinically Acquired Geographic Data," Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 683265, 14 pages, 2012. doi:10.1155/2012/683265en
dc.identifier.doihttps://doi.org/10.1155/2012/683265en
dc.identifier.issn1748-670Xen
dc.identifier.urihttp://hdl.handle.net/10919/48910en
dc.identifier.urlhttp://www.hindawi.com/journals/cmmm/2012/683265/cta/en
dc.language.isoenen
dc.publisherHindawi Publishing Corporationen
dc.rightsCreative Commons Attribution 3.0 Unporteden
dc.rights.holderCopyright © 2012 Tonny J. Oyana et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.subjectSelf-organizing mapsen
dc.subjectPID controlen
dc.subjectPollutionen
dc.subjectDesignen
dc.subjectMathematical & computational biologyen
dc.titleThe New and Computationally Efficient MIL-SOM Algorithm: Potential Benefits for Visualization and Analysis of a Large-Scale High-Dimensional Clinically Acquired Geographic Dataen
dc.title.serialComputational and Mathematical Methods in Medicineen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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