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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

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

Mukesh Prasad
  • Corresponding author
  • Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan, Province of China
/ Yu-Ting Liu
  • Department of Electrical Engineering, National Chiao Tung University, Hsinchu, Taiwan Australia
  • Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia
/ Dong-Lin Li
  • Department of Electrical Engineering, National Chiao Tung University, Hsinchu, Taiwan, Province of China
/ Chin-Teng Lin
  • Department of Electrical Engineering, National Chiao Tung University, Hsinchu, Taiwan Australia
  • Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia
/ Rajiv Ratn Shah
  • School of Computing, National University of Singapore, Singapore
/ Om Prakash Kaiwartya
  • Faculty of Computing, Universiti Teknologi Malaysia, Skudai Johor, Malaysia
Published Online: 2016-12-17 | DOI: https://doi.org/10.1515/jaiscr-2017-0003

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.

Keywords: fuzzy interference system; collaborative clustering; fuzzy logic; big data; data visualization

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About the article

Published Online: 2016-12-17

Published in Print: 2017-01-01



Citation Information: Journal of Artificial Intelligence and Soft Computing Research, ISSN (Online) 2083-2567, DOI: https://doi.org/10.1515/jaiscr-2017-0003. Export Citation

© 2016. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)

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