Integrating Type-1 Fuzzy and Type-2 Fuzzy Clustering with K-Means for Pre-Processing Input Data in Classification Algorithms
International Journal of Intelligent Information Systems
Volume 3, Issue 6-1, December 2014, Pages: 91-97
Received: Oct. 10, 2014;
Accepted: Oct. 14, 2014;
Published: Nov. 6, 2014
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Vahid Nouri, Department of Computer Engineering, Islamic Azad University, Mashhad Branch, Mashhad, Iran
Mohammad Reza Akbarzadeh, Department of Electrical Engineering, University of Neyshabur, Neyshabur, Iran
Tootoonchi, Alireza Rowhanimanesh, Departments of Electrical and Computer Engineering, Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP), Ferdowsi University of Mashhad, Mashhad, Iran
In several papers, clustering has been used for preprocessing datasets before applying classification algorithms in order to enhance classification results. A strong clustered dataset as input to classification algorithms can significantly improve the computation time. This can be particularly useful in “Big Data” where computation time is equally or more important than accuracy. However, there is a trade-off between computation time (speed) and accuracy among clustering algorithms. Specifically, general type-2 fuzzy c-means (GT2 FCM) is considered to be a highly accurate clustering approach, but it is computationally intensive. To improve its computation time we propose a hybrid clustering algorithm called KFGT2FCM that combines GT2 FCM with two fast algorithms k-means and Fuzzy C-means algorithm for input data preprocessing of classification algorithms. The proposed algorithm shows improved computation time when compared with GT2 FCM on five benchmarks from university of California Irvine (UCI) library.
Mohammad Reza Akbarzadeh,
Tootoonchi, Alireza Rowhanimanesh,
Integrating Type-1 Fuzzy and Type-2 Fuzzy Clustering with K-Means for Pre-Processing Input Data in Classification Algorithms, International Journal of Intelligent Information Systems. Special Issue: Research and Practices in Information Systems and Technologies in Developing Countries.
Vol. 3, No. 6-1,
2014, pp. 91-97.
Ting liu, Jurrus, E., Seyedhosseini, M., Ellisman, M., Tasdizen, T.,Watershed merge tree classification for electron microscopy image segmentation. 21st International Conference on Pattern Recognition (ICPR), 11-15 Nov. 2012.
Zhang Bin,Ma, Guorui,Zhang, Zhi,Qin, Qianqing, Region-based classification by combining MS segmentation and MRF for POLSAR images , Journal of Systems Engineering and Electronics. 2013 (Volume:24, Issue:3).
Zhou Q. ,Tong G. ,Xie D. ,Li B. , A Seismic-Based Feature Extraction Algorithm for Robust Ground Target Classification . Signal Processing Letters, IEEE. 2012 (Volume: 19, Issue: 10).
Garcia Bermudez, F.L., Julian, R.C., Haldane, D.W., Abbeel P., Performance analysis and terrain classification for a legged robot over rough terrain , International Conference on Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ.7-12. 2012, Vilamoura, Portugal.
Nasrollahi P., Jafari S. , Ebrahimi, M., Action classification of humanoid soccer robots using machine learning , 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), 2-3 May 2012, Shiraz, Iran.
B., H.B., N., J.C., Hierarchical classification using a Competitive Neural Network , Eighth International Conference on Natural Computation (ICNC), 2012. 29-31 May 2012, Chongqing ,China.
W. Yang , K. Wang , W. Zuo, Prediction of protein secondary structure using large margin nearest neighbor classification . Advanced Computer Control (ICACC), 2011 3rd International Conference, 18-20 Jan. 2011, Harbin, China.
Yuvaraj, N. ,Vivekanandan, P., An efficient SVM based tumor classification with symmetry Non-negative Matrix Factorization using gene expression data, International Conference on Information Communication and Embedded Systems (ICICES), 2013. 21-22. 2013, Chennai, India.
Swangnetr, M.,Kaber, D.B., Emotional State Classification in Patient–Robot Interaction Using Wavelet Analysis and Statistics-Based Feature Selection, IEEE Transactions on Human-Machine Systems (Volume:43,Issue:1),.2013.
W Cai ;S. Chen ;D. Zhang. A Multi-objective Simultaneous Learning Framework for Clustering and Classification . IEEE Transactions on Neural Networks, Volume: 21 , Issue: 2 2010.
E. R., Pfahringer, B., Holmes, G., Clustering for classification . Information Technology in Asia (CITA 11), 2011 7th International Conference on Digital Object Identifier: 10.1109/CITA.2011.5998839. 2011 , Page(s): 1 – 8.
A. Shahi, R. Binti Atan and M-D. Nasir Sulaiman, An effective fuzzy c-mean and type-2 fuzzy logic for weather forecasting . Journal of Theoretical and Applied Information Technology. 2009, Vol. 5 Issue 5, p550 . Malaysia.
Q. Liang and J. Mendel, Decision Feedback Equalizer for Nonlinear Time-Varying Channels Using Type-2 fizzy Adaptive Filters . Fuzzy Systems, 2000..
G. Zhengetal, A Similarity Measure between Interval Type-2 Fuzzy Sets . Proceedings of the 2010 IEEE, International Conference on Mechatronics and Automation 2011.
Kimito Funatsu and Kiyoshi Hasegawa, New fundamental technologies in data mining. First published January, 2011. Printed in India.
Rui Xu, Donald Wunsch II, Survey of Clustering Algorithms . IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 16, NO. 3, 2005.
Huaxiang Zhang , Jing Lu , Creating ensembles of classifiers via fuzzy clustering and deflection . Fuzzy Sets and Systems, Volume 161, Issue 13, 1 2010, Pages 1790–1802.
Ondrej Linda, Milos Manic, General Type-2 Fuzzy C-Means Algorithm for Uncertain Fuzzy Clustering . Fuzzy Systems, IEEE Transactions. 13 2012, ISSN : 1063-6706.
Frank, A. Asuncion, UCI Machine Learning Repository [http://archive.ics.uci.edu/ml], Irvine, CA: University of California, School of Informatics and Computer Science.
R. Athauda, M. Tissera, C. Fernando. ” Data Mining Applications: Promise and Challenges”. Data Mining and Knowledge Discovery in Real Life Applications, ISBN 978-3-902613-53-0, pp. 438, 2009, I-Tech, Vienna, Austria.
M. H. Fazel Zarandi, I. B. Turksen, O. Torabi Kasbi,” Type-2 fuzzy modeling for desulphurization of steel process”. Expert Systems with Applications 32 (2007) 157–171.
D. Wu , J. Mendel.” Enhanced Karnik-Mendel Algorithms for Interval Type-2 Fuzzy Sets and Systems”. Fuzzy Information Processing Society, 2007. Annual Meeting of the North American.
Der-Chen Lin, Miin-Shen Yang,” A similarity measure between type-2 fuzzy sets with its application to clustering”. Fourth International Conference on Fuzzy Systems and Knowledge Discovery, 2007
Wen-liang Hung, Miin-shen Yang ,” Similarity Measures Between Type-2 Fuzzy Sets”. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. Vol. 12, No. 6 (2004) 827-841.
Miin-Shen Yang, Der-Chen Lin. “On similarity and inclusion measures between type-2 fuzzy sets with an application to clustering”. Computers and Mathematics with Applications 57 (2009) 896_907.
Hwang C.-M., Yang M.-S., Hung W.-L., “On similarity, inclusion measure and entropy between type-2 fuzzy sets”. International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems 2012. Volume 53, Issues 9–10.