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Iterative Bayesian fuzzy clustering toward flexible icon-based assistive software for the disabled

Authors:

Kwang-Hyun Park,

Zeungnam BienAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 180, Issue 3

Pages 325 - 340

https://doi.org/10.1016/j.ins.2009.09.015

Published: 01 February 2010 Publication History

Abstract

A novel fuzzy clustering technique, called iterative Bayesian fuzzy clustering (IBFC), is presented and applied for grouping and recommendation of icons associated with assistive software meant for the physically disabled. The algorithm incorporates a modified fuzzy competitive learning structure with a Bayesian decision rule. In order to ignore unintended behavior of the user, a Bayesian minimum risk classification rule with two loss coefficients is built into the algorithm. This provides a rational basis for outlier detection in noisy data. In addition, we show that the inclusion of a unique control parameter of IBFC allows for establishment of a strong relationship between learning region and cluster congestion. This interpretation leads to an agglomerative iterative Bayesian fuzzy clustering (AIBFC) framework capable of clustering data of complex structure. The proposed AIBFC framework is applied to design a flexible interface for the icon-based assistive software for the disabled. The latter is utilized in grouping and recommendation of icons. Additionally, the proposed algorithm is shown to outperform several well-known methods for both IRIS and Wisconsin benchmark data sets. Finally, it is shown, using a questionnaire survey of real end-users, that the software designed using AIBFC framework meets users' needs.

References

[1]

Z.Z. Bien, Learning system techniques for human-friendly man-machine interaction, in: Keynote Speech in the 2nd International Conference on Artificial Intelligence in Engineering and Technology, Malaysia, August 3-5, 2004.

[2]

Buckley, J.J., . 2006. Fuzzy Probability and Statistics: Studies in Fuzziness and Soft Computing, 2006.Springer.

[3]

Camastra, F. and Verri, A., A novel kernel method for clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence. v27. 801-805.

Digital Library

[4]

Carpenter, G.A. and Grossberg, S., Massively parallel architecture for a self-organizing neural pattern recognition machine. Computer Vision, Graphics, and Image Processing. v37. 54-115.

[5]

Carpenter, G.A., Grossberg, S. and Rosen, D.B., Fuzzy ART: fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks. v4. 759-771.

Digital Library

[6]

Chung, F.L. and Lee, T., Fuzzy competitive learning. Neural Networks. v7. 539-551.

Digital Library

[7]

F.-L. Chung, T. Lee, Fuzzy learning vector quantization, in: Proceedings of IEEE International Joint Conference on Neural Networks, Japan, Nagoya, 1993, pp. 2739-2742.

[8]

Czogala, E. and Pedrycz, W., On the concept of fuzzy probabilistic controllers. Fuzzy Sets and Systems. v10. 109-121.

Digital Library

[9]

Duda, R.O., Hart, P.E. and stork, D.G., Pattern Classification. 2001. second ed. John Wiley& Sons.

[10]

Rumelhart, D.E. and McClelland, J.L., . 1987. The PDP Research Group, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Foundations, 1987.MIT Press.

[11]

Jang, J.-S.R., Sun, C.-T. and Mizutani, E., Neuro-Fuzzy and Soft Computing. 1997. Prentice Hall.

[12]

Johnson, R.A. and Winchern, D.W., Applied Multivariate Statistical Analysis. 2002. Pearson Education.

[13]

Kandel, A., Martins, A. and Pacheco, R., Discussion: on the very real distinction between fuzzy and statistical methods. Technometrics. v37. 276-281.

[14]

Karayiannis, N.B. and Bezdek, J.C., An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering. IEEE Transactions on Fuzzy Systems. v5. 622-628.

Digital Library

[15]

Keller, J.M., Gray, M.R. and givens JR., J.A., A fuzzy K-nearest neighbor algorithm. IEEE Transactions on Systems, Man, and Cybernetics. v15. 580-585.

[16]

W.E. Kelly, Dimensionality in Fuzzy Systems, Ph.D. Dissertation, Texas A& M University, Department of Electrical Engineering, College Station, Texas, August 1997.

[17]

W. Kelly, J. Painter, Hypertrapezoidal fuzzy membership functions for decision aiding, in: Second On-line World Conference on Soft Computing, UK, June 23-27, 1997.

[18]

Y.S. Kim, An unsupervised neural network using a fuzzy learning rule, in: Proceedings of IEEE International Conference on Fuzzy Systems, Seoul, Korea, vol. 1, 1999, pp. 349-353.

[19]

Y.S. Kim, S. Mitra, Integrated adaptive fuzzy clustering algorithm, in: Proceedings of the Second IEEE International Conference on Fuzzy Systems, vol. 2, San Francisco, CA, 1993, pp. 1264-1268.

[20]

Kim, Y.S. and Mitra, S., An adaptive integrated fuzzy clustering model for pattern recognition. Fuzzy Sets and Systems. v65. 297-310.

Digital Library

[21]

Kim, D.-W., Lee, K.Y., Lee, D. and Lee, K.H., Evaluation of the performance of clustering algorithms in kernel-induced feature space. Pattern Recognition. v38. 607-611.

Digital Library

[22]

Kohonen, T., Self-Organization and Associative Memory. 1989. third ed. Springer-Verlag.

[23]

Kosmala, W.A.J., Introductory Mathematical Analysis. 1995. W.C. Brown Publishers, Inc.

[24]

Kraryiannis, N.B., A methodology for constructing fuzzy algorithms for learning vector quantization. IEEE Transactions on Neural Networks. v8. 505-518.

Digital Library

[25]

Krishnapuram, R. and Keller, J., A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems. v1. 98-110.

Digital Library

[26]

Kuncheva, L.I. and Vetrov, D.P., Evaluation of stability of k-means cluster ensembles with respect to random initialization. IEEE Transactions on Pattern Analysis and Machine Intelligence. v28. 1798-1808.

Digital Library

[27]

B. Kusmoputro, M.R. Widyanto, M.I. Fanany, H. Budiarto, Improvement of artificial odor discrimination system using fuzzy-LVQ neural network, in: Proceedings of the Third International Conference on Computational Intelligence and Multimedia Applications, 1999, pp. 474-478.

[28]

S.W. Lee, Y.S. Kim, Z. Bien, Bayesian interpretation of adaptive fuzzy neural network, in: Proceedings of IEEE International Conference on Fuzzy Systems, BC, Canada, 2006, pp. 2178-2183.

[29]

Lee, H.-W. and Bien, Z., Iterative fuzzy clustering algorithm with supervision to construct probabilistic fuzzy rule base from numerical data. IEEE Transactions on Fuzzy Systems. v16. 263-277.

[30]

Ozdemir, R.G., Gencyilmaz, G. and Aktin, T., The modified fuzzy art and a two-stage clustering approach to cell design. Information Sciences. v177. 219-5236.

Digital Library

[31]

Pal, N.R., Pal, K., Keller, J.M. and Bezdek, J.C., A possibilistic fuzzy c-means clustering algorithm. IEEE Transactions on Fuzzy Systems. v13. 517-530.

[32]

Simpson, P.K., Fuzzy min-max neural networks - Part 2: clustering. IEEE Transactions on Fuzzy Systems. v1. 32-45.

Digital Library

[33]

Tsao, E.C., Bezdek, J.C. and Pal, N.R., Fuzzy Kohonen clustering network. Pattern Recognition. v27. 757-764.

[34]

Xu, Z., Chen, J. and Wu, J., Clustering algorithm for intuitionistic fuzzy sets. Information Sciences. v178. 3775-3790.

Digital Library

[35]

Yip, A.M., Ding, C. and Chan, T.F., Dynamic cluster formation using level set methods. IEEE Transactions on Pattern Analysis and Machine Intelligence. v28. 877-889.

Digital Library

Cited By

Devi RKannan SHong TRamathilagam S(2015)Fuzzy Clustering Systems in Analyzing High Dimensional DatabaseProceedings of the ASE BigData & SocialInformatics 201510.1145/2818869.2818879(1-4)Online publication date: 7-Oct-2015
https://dl.acm.org/doi/10.1145/2818869.2818879
Glenn TZare AGader P(2015)Bayesian Fuzzy ClusteringIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2014.237067623:5(1545-1561)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.1109/TFUZZ.2014.2370676
Ros MCuéLlar MDelgado MVila A(2013)Online recognition of human activities and adaptation to habit changes by means of learning automata and fuzzy temporal windowsInformation Sciences: an International Journal10.1016/j.ins.2011.10.005220(86-101)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.1016/j.ins.2011.10.005

Iterative Bayesian fuzzy clustering toward flexible icon-based assistive software for the disabled
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning

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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 180, Issue 3

February, 2010

157 pages

ISSN:0020-0255

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Copyright © Elsevier Inc. © 2009.

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Elsevier Science Inc.

United States

Publication History

Published: 01 February 2010

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Cited By

Devi RKannan SHong TRamathilagam S(2015)Fuzzy Clustering Systems in Analyzing High Dimensional DatabaseProceedings of the ASE BigData & SocialInformatics 201510.1145/2818869.2818879(1-4)Online publication date: 7-Oct-2015
https://dl.acm.org/doi/10.1145/2818869.2818879
Glenn TZare AGader P(2015)Bayesian Fuzzy ClusteringIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2014.237067623:5(1545-1561)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.1109/TFUZZ.2014.2370676
Ros MCuéLlar MDelgado MVila A(2013)Online recognition of human activities and adaptation to habit changes by means of learning automata and fuzzy temporal windowsInformation Sciences: an International Journal10.1016/j.ins.2011.10.005220(86-101)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.1016/j.ins.2011.10.005

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