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Autonomous and Deterministic Probabilistic Neural Network Using Global k-Means

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3971))

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Abstract

We present a comparative study between Expectation-Maximization (EM) trained probabilistic neural network (PNN) with random initialization and with initialization from Global k-means. To make the results more comprehensive, the algorithm was tested on both homoscedastic and heteroscedastic PNNs. Normally, user have to define the number of clusters through trial and error method, which makes random initialization to be of stochastic nature. Global k-means was chosen as the initialization method because it can autonomously find the number of clusters using a selection criterion and can provide deterministic clustering results. The proposed algorithm was tested on benchmark datasets and real world data from the cooling water system in a power plant.

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References

  1. Specht, D.F.: Probabilistic Neural Network. Neural Networks 3, 109–118 (1990)

    Article  Google Scholar 

  2. Likas, A., Vlassis, N., Verbeek, J.J.: The Global k-means Clustering Algorithm. Pattern Recognition 36, 451–461 (2003)

    Article  Google Scholar 

  3. Wu, C.: On the Convergence Properties of the EM Algorithm. Annals Statistics 11, 95–103 (1983)

    Article  MATH  Google Scholar 

  4. Xu, L., Jordan, M.I.: On Convergence Properties of the EM Algorithm for Gaussian Mixtures. Neural Computation 8, 129–151 (1996)

    Article  Google Scholar 

  5. Yang, Z.R., Chen, S.: Robust Maximum Likelihood Training of Heteroscedastic Probabilistic Neural Networks. Neural Networks 11, 739–747 (1998)

    Article  Google Scholar 

  6. Ordonez, C., Omiecinski, E.: FREM: Fast and Robust EM Clustering for Large Data Sets. In: Proc. of the 11th CIKM, Clustering Algorithms., pp. 590–599. ACM Press, New York (2002)

    Google Scholar 

  7. Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases. Department of Information and Computer Sciences. University of California, Irvine (1998)

    Google Scholar 

  8. Zarndt, F.: A Comprehensive Case Study: An Examination of Machine Learning and Connectionist Algorithms. MSc Thesis. Dept. of Computer Science, Brigham Young University (1995)

    Google Scholar 

  9. Ster, B., Dobnikar, A.: Neural Networks in Medical Diagnosis: Comparison with Other Methods. In: Bulsari, A., et al. (eds.) Proc. Int. Conference EANN 1996. Solving Engineering Problems with Neural Networks, pp. 427–430. Systems Engineering Association, Turku (1996)

    Google Scholar 

  10. Chen, K.Y., Lim, C.P., Lai, W.K.: Application of a Neural Fuzzy System with Rule Extraction to Fault Detection and Diagnosis. Journal of Intelligent Manufacturing 16, 679–691 (2005)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Information Science and Technology, Mutilmedia University, Jalan Ayer Keroh Lama, 75450, Melaka, Malaysia

    Roy Kwang Yang Chang

  2. Faculty of Engineering Technology, Mutilmedia University, Jalan Ayer Keroh Lama, 75450, Melaka, Malaysia

    Chu Kiong Loo & Machavaram V. C. Rao

Authors
  1. Roy Kwang Yang Chang
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  2. Chu Kiong Loo
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  3. Machavaram V. C. Rao
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Chang, R.K.Y., Loo, C.K., Rao, M.V.C. (2006). Autonomous and Deterministic Probabilistic Neural Network Using Global k-Means. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_122

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  • DOI: https://doi.org/10.1007/11759966_122

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34439-1

  • Online ISBN: 978-3-540-34440-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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