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Algorithms 2018, 11(5), 56; https://doi.org/10.3390/a11050056

BELMKN: Bayesian Extreme Learning Machines Kohonen Network

1
Robotics Advance Lab, School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
2
Department of Computer Science Engineering, BMS College of Engineering, Bengaluru 560019, India
3
Wipro Technologies, Keonics Electronic City, Bengaluru 560100, India
*
Author to whom correspondence should be addressed.
Received: 1 April 2018 / Revised: 22 April 2018 / Accepted: 24 April 2018 / Published: 27 April 2018
(This article belongs to the Special Issue Advanced Artificial Neural Networks)
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Abstract

This 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. View Full-Text
Keywords: clustering; bayesian information criteria; extreme learning machine; Kohonen network clustering; bayesian information criteria; extreme learning machine; Kohonen network
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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. (CC BY 4.0).
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Senthilnath, J.; Simha C, S.; G, N.; Thapa, M.; M, I. BELMKN: Bayesian Extreme Learning Machines Kohonen Network. Algorithms 2018, 11, 56.

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