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

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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.

[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. 1279-1284,1994.

[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.

Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

Journal Information

CiteScore 2017: 5.00

SCImago Journal Rank (SJR) 2017: 0.492
Source Normalized Impact per Paper (SNIP) 2017: 2.813

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