Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87582
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dc.contributor.authorSimha C, Sumanthen
dc.contributor.authorG, Nagarajen
dc.contributor.authorThapa, Meenakumarien
dc.contributor.authorM, Indirammaen
dc.contributor.authorSenthilnath, Jayaveluen
dc.date.accessioned2018-08-02T07:20:10Zen
dc.date.accessioned2019-12-06T16:44:58Z-
dc.date.available2018-08-02T07:20:10Zen
dc.date.available2019-12-06T16:44:58Z-
dc.date.issued2018en
dc.identifier.citationSenthilnath, J., Simha C, S., G, N., Thapa, M., & M, I. (2018). BELMKN : Bayesian extreme learning machines Kohonen Network. Algorithms, 11(5), 56-.en
dc.identifier.issn1999-4893en
dc.identifier.urihttps://hdl.handle.net/10356/87582-
dc.identifier.urihttp://hdl.handle.net/10220/45436en
dc.description.abstractThis paper proposes the Bayesian Extreme Learning Machine Kohonen Network (BELMKN) framework to solve the clustering problem. The BELMKN framework uses three levels in processing nonlinearly separable datasets to obtain efficient clustering in terms of accuracy. In the first level, the Extreme Learning Machine (ELM)-based feature learning approach captures the nonlinearity in the data distribution by mapping it onto a d-dimensional space. In the second level, ELM-based feature extracted data is used as an input for Bayesian Information Criterion (BIC) to predict the number of clusters termed as a cluster prediction. In the final level, feature-extracted data along with the cluster prediction is passed to the Kohonen Network to obtain improved clustering accuracy. The main advantage of the proposed method is to overcome the problem of having a priori identifiers or class labels for the data; it is difficult to obtain labels in most of the cases for the real world datasets. The BELMKN framework is applied to 3 synthetic datasets and 10 benchmark datasets from the UCI machine learning repository and compared with the state-of-the-art clustering methods. The experimental results show that the proposed BELMKN-based clustering outperforms other clustering algorithms for the majority of the datasets. Hence, the BELMKN framework can be used to improve the clustering accuracy of the nonlinearly separable datasets.en
dc.format.extent14 p.en
dc.language.isoenen
dc.relation.ispartofseriesAlgorithmsen
dc.rights© 2018 The Author(s). Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en
dc.subjectClusteringen
dc.subjectBayesian Information Criteriaen
dc.titleBELMKN : Bayesian extreme learning machines Kohonen Networken
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.identifier.doi10.3390/a11050056en
dc.description.versionPublished versionen
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