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International Symposium on Neural Networks

ISNN 2015: Advances in Neural Networks – ISNN 2015 pp 382–389Cite as

A Novel K-Means Evolving Spiking Neural Network Model for Clustering Problems

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9377))

Abstract

In this paper, a novel K-means evolving spiking neural network (K-ESNN) model for clustering problems has been presented. K-means has been utilised to improve the original ESNN model. This model enhances the flexibility of the ESNN algorithm in producing better solutions to overcoming the disadvantages of K-means. Several standard data sets from UCI machine learning are used for evaluating the performance of this model. It has been found that the K-ESNN provides competitive results in clustering accuracy and speed performance measures compared to the standard K-means. More discussion is provided to prove the effectiveness of the new model in clustering problems.

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Author information

Authors and Affiliations

  1. Soft Computing Research Group, Faculty of Computing, Universiti Teknologi Malaysia (UTM), Skudai, 81310, Johor, Malaysia

    Haza Nuzly Abdull Hamed

  2. UTM Big Data Centre, Universiti Teknologi Malaysia (UTM), Skudai, 81310, Johor, Malaysia

    Abdulrazak Yahya Saleh & Siti Mariyam Shamsuddin

Authors
  1. Haza Nuzly Abdull Hamed
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  2. Abdulrazak Yahya Saleh
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  3. Siti Mariyam Shamsuddin
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Corresponding author

Correspondence to Haza Nuzly Abdull Hamed .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Xiaolin Hu

  2. Fuzhou University, Fuzhou, China

    Yousheng Xia

  3. School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China

    Yunong Zhang

  4. Chinese Academy of Sciences, Institute of Automation, Beijing, China

    Dongbin Zhao

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© 2015 Springer International Publishing Switzerland

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Hamed, H.N.A., Saleh, A.Y., Shamsuddin, S.M. (2015). A Novel K-Means Evolving Spiking Neural Network Model for Clustering Problems. In: Hu, X., Xia, Y., Zhang, Y., Zhao, D. (eds) Advances in Neural Networks – ISNN 2015. ISNN 2015. Lecture Notes in Computer Science(), vol 9377. Springer, Cham. https://doi.org/10.1007/978-3-319-25393-0_42

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  • DOI: https://doi.org/10.1007/978-3-319-25393-0_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25392-3

  • Online ISBN: 978-3-319-25393-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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