Unsupervised functional link artificial neural networks for cluster analysis

Bhabani Shankar Prasad Mishra; Om Pandey; Satchidananda Dehuri; Sung Bae Cho

doi:10.1109/ACCESS.2020.3024111

Unsupervised functional link artificial neural networks for cluster analysis

Bhabani Shankar Prasad Mishra, Om Pandey, Satchidananda Dehuri, Sung Bae Cho

College of Computing

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

In this paper, we propose a novel method of cluster analysis called unsupervised functional link artificial neural networks (UFLANNs), which inherit the best characteristics of functional link artificial neural networks and self-organizing feature maps (SOFMs). UFLANNs adopt three types of basis functions such as Chebyshev, Legendre orthogonal polynomials, and power series for mapping the input data into a new feature space with higher dimensions, where the objects are clustered based on the principle of competitive learning of SOFMs. The effectiveness of this algorithm has been tested with various artificial and real-life datasets including remote sensing images. A thorough comparison with other popular clustering algorithms shows that the proposed method is promising in revealing clusters from many complex datasets.

Original language	English
Pages (from-to)	169215-169228
Number of pages	14
Journal	IEEE Access
Volume	8
DOIs	https://doi.org/10.1109/ACCESS.2020.3024111
Publication status	Published - 2020

Bibliographical note

Funding Information:
This work was supported in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant funded by the Korean Government (MSIT), Artificial Intelligence Graduate School Program, Yonsei University, under Grant 2020-0-01361.

Publisher Copyright:
© This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

All Science Journal Classification (ASJC) codes

Computer Science(all)
Materials Science(all)
Engineering(all)

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10.1109/ACCESS.2020.3024111

Cite this

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title = "Unsupervised functional link artificial neural networks for cluster analysis",

abstract = "In this paper, we propose a novel method of cluster analysis called unsupervised functional link artificial neural networks (UFLANNs), which inherit the best characteristics of functional link artificial neural networks and self-organizing feature maps (SOFMs). UFLANNs adopt three types of basis functions such as Chebyshev, Legendre orthogonal polynomials, and power series for mapping the input data into a new feature space with higher dimensions, where the objects are clustered based on the principle of competitive learning of SOFMs. The effectiveness of this algorithm has been tested with various artificial and real-life datasets including remote sensing images. A thorough comparison with other popular clustering algorithms shows that the proposed method is promising in revealing clusters from many complex datasets.",

author = "Mishra, {Bhabani Shankar Prasad} and Om Pandey and Satchidananda Dehuri and Cho, {Sung Bae}",

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AU - Pandey, Om

AU - Dehuri, Satchidananda

AU - Cho, Sung Bae

N1 - Funding Information: This work was supported in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant funded by the Korean Government (MSIT), Artificial Intelligence Graduate School Program, Yonsei University, under Grant 2020-0-01361. Publisher Copyright: © This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

PY - 2020

Y1 - 2020

N2 - In this paper, we propose a novel method of cluster analysis called unsupervised functional link artificial neural networks (UFLANNs), which inherit the best characteristics of functional link artificial neural networks and self-organizing feature maps (SOFMs). UFLANNs adopt three types of basis functions such as Chebyshev, Legendre orthogonal polynomials, and power series for mapping the input data into a new feature space with higher dimensions, where the objects are clustered based on the principle of competitive learning of SOFMs. The effectiveness of this algorithm has been tested with various artificial and real-life datasets including remote sensing images. A thorough comparison with other popular clustering algorithms shows that the proposed method is promising in revealing clusters from many complex datasets.

AB - In this paper, we propose a novel method of cluster analysis called unsupervised functional link artificial neural networks (UFLANNs), which inherit the best characteristics of functional link artificial neural networks and self-organizing feature maps (SOFMs). UFLANNs adopt three types of basis functions such as Chebyshev, Legendre orthogonal polynomials, and power series for mapping the input data into a new feature space with higher dimensions, where the objects are clustered based on the principle of competitive learning of SOFMs. The effectiveness of this algorithm has been tested with various artificial and real-life datasets including remote sensing images. A thorough comparison with other popular clustering algorithms shows that the proposed method is promising in revealing clusters from many complex datasets.

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Unsupervised functional link artificial neural networks for cluster analysis

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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