Advertisement

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

  • Haza Nuzly Abdull Hamed
  • Abdulrazak Yahya Saleh
  • Siti Mariyam Shamsuddin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Clustering Evolving Spiking Neural Networks K-ESNN K-means Spiking Neural Network 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Schliebs, S., Defoin-Platel, M., Worner, S., Kasabov, N.: Integrated feature and parameter optimization for an evolving spiking neural network: Exploring heterogeneous probabilistic models. Neural Networks 22, 623–632 (2009)CrossRefGoogle Scholar
  2. 2.
    Kasabov, N.K.: NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data. Neural Networks 52, 62–76 (2014)CrossRefGoogle Scholar
  3. 3.
    MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA, pp. 281–297 (1967)Google Scholar
  4. 4.
    Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Philip, S.Y.: Top 10 algorithms in data mining. Knowledge and Information Systems 14, 1–37 (2008)CrossRefGoogle Scholar
  5. 5.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.: Advances in knowledge discovery and data mining (1996)Google Scholar
  6. 6.
    Firouzi, B., Niknam, T., Nayeripour, M.: A new evolutionary algorithm for cluster analysis. World Academy of Science, Engineering, and Technology 36, 605–609 (2008)Google Scholar
  7. 7.
    Wu, J.: Advances in K-means clustering: a data mining thinking. Springer Science & Business Media, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recognition Letters 31, 651–666 (2010)CrossRefGoogle Scholar
  9. 9.
    Berkhin, P.: A survey of clustering data mining techniques. In: Grouping Multidimensional Data, pp. 25–71. Springer (2006)Google Scholar
  10. 10.
    Bock, H.-H.: Clustering methods: a history of k-means algorithms. In: Selected Contributions in Data Analysis and Classification, pp. 161-172. Springer (2007)Google Scholar
  11. 11.
    Patel, V.R., Mehta, R.G.: Modified k-means clustering algorithm. In: Das, V.V. (ed.) CIIT 2011. CCIS, vol. 250, pp. 307–312. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Mandloi, M.: A Survey on Clustering Algorithms and K-Means (July 2014)Google Scholar
  13. 13.
    Kotsiantis, S., Pintelas, P.: Recent advances in clustering: A brief survey. WSEAS Transactions on Information Science and Applications 1, 73–81 (2004)Google Scholar
  14. 14.
    Schliebs, S., Kasabov, N.: Evolving spiking neural network—a survey. Evolving Systems 4, 87–98 (2013)CrossRefGoogle Scholar
  15. 15.
    Abdull Hamed, H.N., Kasabov, N., Michlovský, Z., Shamsuddin, S.M.: String Pattern Recognition Using Evolving Spiking Neural Networks and Quantum Inspired Particle Swarm Optimization. In: Leung, C.S., Lee, M., Chan, J.H. (eds.) ICONIP 2009, Part II. LNCS, vol. 5864, pp. 611–619. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  16. 16.
    Hamed, H.N., Kasabov, N.: Quantum-inspired particle swarm optimisation for integrated feature and parameter optimisation of evolving spiking neural networks. International Journal of Artificial Intelligence 7, 114–124 (2011)Google Scholar
  17. 17.
    Hamed, H.N., Kasabov, N., Shamsuddin, S.M., Widiputra, H., Dhoble, K.: An extended evolving spiking neural network model for spatio-temporal pattern classification. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 2653–2656. IEEE (2011)Google Scholar
  18. 18.
    Saleh, A.Y., Hameed, H.N.B.A., Najib, M., Salleh, M.: A Novel hybrid algorithm of Differential evolution with Evolving Spiking Neural Network for pre-synaptic neurons Optimization. Int. J. Advance Soft Compu. Appl. 6 (2014)Google Scholar
  19. 19.
    Saleh, A.Y., Shamsuddin, S.M., Hamed, H.N.B.A.: Parameter Tuning of Evolving Spiking Neural Network with Differen-tial Evolution Algorithm. In: International Conference of Recent Trends in Information and Communication Technologies, vol. 13 (2014)Google Scholar
  20. 20.
    Bohte, S.M., Kok, J.N., La Poutre, H.: Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing 48, 17–37 (2002)CrossRefGoogle Scholar
  21. 21.
    Thorpe, S.: How can the human visual system process a natural scene in under 150ms? experiments and neural network models, pp. 2-9600049. D-Facto public, ISBN (1997)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

<SimplePara><Emphasis Type="Bold">Open Access</Emphasis> This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. </SimplePara> <SimplePara>The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.</SimplePara>

Authors and Affiliations

  • Haza Nuzly Abdull Hamed
    • 1
  • Abdulrazak Yahya Saleh
    • 2
  • Siti Mariyam Shamsuddin
    • 2
  1. 1.Soft Computing Research Group, Faculty of ComputingUniversiti Teknologi Malaysia (UTM)SkudaiMalaysia
  2. 2.UTM Big Data CentreUniversiti Teknologi Malaysia (UTM)SkudaiMalaysia

Personalised recommendations