A New Mechanism for Data Visualization with Tsk-Type Preprocessed Collaborative Fuzzy Rule Based System

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Abstract

A novel data knowledge representation with the combination of structure learning ability of preprocessed collaborative fuzzy clustering and fuzzy expert knowledge of Takagi- Sugeno-Kang type model is presented in this paper. The proposed method divides a huge dataset into two or more subsets of dataset. The subsets of dataset interact with each other through a collaborative mechanism in order to find some similar properties within each-other. The proposed method is useful in dealing with big data issues since it divides a huge dataset into subsets of dataset and finds common features among the subsets. The salient feature of the proposed method is that it uses a small subset of dataset and some common features instead of using the entire dataset and all the features. Before interactions among subsets of the dataset, the proposed method applies a mapping technique for granules of data and centroid of clusters. The proposed method uses information of only half or less/more than the half of the data patterns for the training process, and it provides an accurate and robust model, whereas the other existing methods use the entire information of the data patterns. Simulation results show the proposed method performs better than existing methods on some benchmark problems.

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  • [1] E. H. Mamdani and S. Assilian An experiment in linguistic synthesis with a fuzzy logic controller International Journal of Man-machine Studies vol. 7 pp. 1-13 1975.

  • [2] T. Takagi and M. Sugeno Fuzzy identification of systems and its applications to modeling and control IEEE Transaction on System Man and Cybernetic vol. 15 pp. 116-132 1985.

  • [3] L. Rutkowski and K. Cpaka Designing and learning of adjustable quasi-triangular norms with applications to neuro-fuzzy systems IEEE Transaction on Fuzzy Systems vol. 13 no. 1 pp. 140-151 2005.

  • [4] L. Rutkowski and K. Cpaka Flexible neuro-fuzzy systems IEEE Transaction on Neural Networks vol. 14 no. 3 pp. 554-574 2003.

  • [5] J. C. Bezdek Pattern recognition with fuzzy objective function algorithms Plenum Press New York 1981.

  • [6] J. C. Bezdek R. Ehrlich and W. Full FCM: the fuzzy C-means clustering algorithm Computers and Geosciences vol. 10 no. 2-3 pp. 191-203 1984.

  • [7] W. Pedrycz Knowledge-based clustering: from data to information granules A JohnWiley & Sons Inc. Publication 2005.

  • [8] W. Pedrycz Collaborative fuzzy clustering Pattern Recognition Letters vol. 23 no. 14 pp. 1675-1686 2002.

  • [9] W. Pedrycz and P. Rai Collaborative Fuzzy Clustering with the use of Fuzzy C-Means and its Quantification Fuzzy Sets and System vol. 159 no. 18 pp. 2399-2427 2008.

  • [10] C. T. Lin M. Prasad and J. Y Chang Designing Mamdani Type Fuzzy Rule Using a Collaborative FCM Scheme International Conference on Fuzzy Theory and Its Application 2013.

  • [11] http://www.mathworks.com/help/fuzzy/genfis2.html

  • [12] R. Babuska Fuzzy Modeling for Control Norwell MA: Kluwer 1998.

  • [13] J. C. Dunn A fuzzy relative of the ISODATA process and its use in detecting compact well separated clusters Journal of Cybernetics vol. 3 pp. 32-57 1973.

  • [14] F. Hoppner F. Klawonn R. Kruse and T. Runkler Fuzzy Cluster Analysis: Methods for Classification. Data Analysis and Image Recognition New York: Wiley 1999.

  • [15] E. R. Hruschka R. J. G. B. Campello A. A. Freitas and A. C. P. L. F. de Carvalho A survey of evolutionary algorithms for clustering IEEE Transaction on System Man Cybernetics- part-c vol. 39 no. 2 pp. 133-155 2009.

  • [16] R. Xu and D. Wunsch Survey of clustering algorithms IEEE Transaction on Neural Networks vol. 16 no. 3 pp. 645-678 2005.

  • [17] R. R. Yager and D. P. Filev Approximate clustering via the mountain method IEEE Transaction on System Man Cybernetics vol. 24 no. 8 pp. 1279-12841994.

  • [18] P. R. Kersten Implementation issues in the fuzzy c-medians clustering algorithm In Proceeding 6th IEEE International Conference on Fuzzy Systems vol. 2 pp. 957-962 1997.

  • [19] J. F. Kolen and T. Hutcheson Reducing the time complexity of the fuzzy c-means algorithm IEEE Transaction on Fuzzy Systems vol. 10 no. 2 pp. 263-267 2002.

  • [20] M. Sugeno and G. T. Kang Structure identification of fuzzy model Fuzzy Sets Systems vol. 28 no. 1 pp. 15-33 1988.

  • [21] C. W. Ting and C. Quek A Novel Blood Glucose Regulation Using - TSK FCMAC: A Fuzzy CMAC Based on the Zero-Ordered TSK Fuzzy Inference Scheme IEEE Transaction on Neural Networks vol. 20 no. 5 pp. 856-871 2009.

  • [22] J. R. Castro O. Castillo P. Melin and A. Rodrguez-Daz A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks Information Science vol. 179 no. 13 pp. 2175-2193 2009.

  • [23] H. Song C. Miao Z. Shen Y. Miao and B. S. Lee A fuzzy neural network with fuzzy impact grades Neurocomputing vol. 72 no. 13-15 pp. 3098-3122 2009.

  • [24] D. Kim and C. Kim Forecasting time series with genetic fuzzy predictor ensembles IEEE Transaction on Fuzzy Systems vol. 5 no. 4 pp. 523-535 1997.

  • [25] M. Prasad C. T. Lin C. T. Yang and A. Saxena Vertical Collaborative Fuzzy C-Means for Multiple EEG Data Sets Springer Lecture Notes in Computer Science vol. 8102 pp. 246-257 2013.

  • [26] R. N. Dave and K. Bhaswan Adaptive fuzzy cshells clustering and detection of ellipses IEEE Transaction on Neural Networks vol. 3 no. 5 pp. 643-662 1992.

  • [27] Y. Man and I. Gath Detection and separation of ring-shaped clusters using fuzzy clustering IEEE Transaction on Pattern Analysis and Machine Intelligence vol. 16 no. 8 pp. 855-861 1994.

  • [28] R. Krishnapuram O. Nasraoui and J. Keller The fuzzy c spherical shells algorithm: a new approach IEEE Transaction on Neural Networks vol. 3 no. 5 pp. 663-671 1992.

  • [29] M. Prasad D. L. Li Y. T. Liu L. Siana C. T. Lin and A. Saxena A Preprocessed Induced Partition Matrix Based Collaborative Fuzzy Clustering for Data Analysis IEEE International Conference of Fuzzy Systems pp. 1553-1558 2014.

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