Robust Dataset Classification Approach Based on Neighbor Searching and Kernel Fuzzy C-Means

Li Liu; Aolei Yang; Wenju Zhou; Xiaofeng Zhang; Minrui Fei; Xiaowei Tu

Volume 2 Issue 3

Jul. 2015

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

CiteScore: 23.5, Top 2% (Q1)
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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2015 > 2(3): 235-247

Li Liu, Aolei Yang, Wenju Zhou, Xiaofeng Zhang, Minrui Fei and Xiaowei Tu, "Robust Dataset Classification Approach Based on Neighbor Searching and Kernel Fuzzy C-Means," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 3, pp. 235-247, 2015.

Citation:

Li Liu, Aolei Yang, Wenju Zhou, Xiaofeng Zhang, Minrui Fei and Xiaowei Tu, "Robust Dataset Classification Approach Based on Neighbor Searching and Kernel Fuzzy C-Means," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 3, pp. 235-247, 2015.

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Robust Dataset Classification Approach Based on Neighbor Searching and Kernel Fuzzy C-Means

1. Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China;
2. School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK;
3. School of Information Science and Electrical Engineering, Ludong University, Yantai 264025, China

Funds:

This work was supported by National Natural Science Foundation of China (61403244, 61304031), Key Project of Science and Technology Commission of Shanghai Municipality (14JC1402200), the Shanghai Municipal Commission of Economy and Informatization under Shanghai Industry-University- Research Collaboration (CXY-2013-71), the Science and Technology Commission of Shanghai Municipality under 'Yangfan Program' (14YF1408600), National Key Scientific Instrument and Equipment Development Project (2012YQ15008703), and Innovation Program of Shanghai Municipal Education Commission (14YZ007).

Abstract

Abstract

Dataset classification is an essential fundament of computational intelligence in cyber-physical systems (CPS). Due to the complexity of CPS dataset classification and the uncertainty of clustering number, this paper focuses on clarifying the dynamic behavior of acceleration dataset which is achieved from micro electro mechanical systems (MEMS) and complex image segmentation. To reduce the impact of parameters uncertainties with dataset classification, a novel robust dataset classification approach is proposed based on neighbor searching and kernel fuzzy c-means (NSKFCM) methods. Some optimized strategies, including neighbor searching, controlling clustering shape and adaptive distance kernel function, are employed to solve the issues of number of clusters, the stability and consistency of classification, respectively. Numerical experiments finally demonstrate the feasibility and robustness of the proposed method.
- Dataset classification,
- neighbor searching,
- variable weight,
- kernel fuzzy c-means,
- robustness estimation

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References

[1]	Dunn J. A graph theoretic analysis of pattern classification via Tamura's fuzzy relation. IEEE Transactions on Systems, Man, and Cybernetics, 1974, SMC-4(3):310-313
[2]	Bezdek J C. Pattern Recognition with Fuzzy Objective Function Algorithms. New York:Springer, 1981.
[3]	Wu K L, Yang M S. Alternative c-means clustering algorithms. Pattern Recognition, 2002, 35(10):2267-2278
[4]	Ahmed M N, Yamany S M, Mohamed N, Farag A A, Moriarty T. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Transactions on Medical Imaging, 2002, 21(3):193-199
[5]	Chen S C, Zhang D Q. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics, 2004, 34(4):1907-1916
[6]	Szilagyi L, Benyo Z, Szilagyi S M, Adam H S. MR brain image segmentation using an enhanced fuzzy c-means algorithm. In:Proceedings of the 25th Annual International Conference on Engineering in Medicine and Biology Society. Cancun, Mexico:IEEE, 2003. 724-726
[7]	Li C F, Liu L Z, Jiang W L. Objective function of semi-supervised fuzzy c-means clustering algorithm. In:Proceedings of the 6th IEEE International Conference on Industrial Informatics. Daejeon, Korea:IEEE, 2008. 737-742
[8]	Huang S B, Cheng Y, Wan Q S, Liu G F, Shen L S. A hierarchical multi-relational clustering algorithm based on IDEF1x. Acta Automatica Sinica, 2014, 40(8):1740-1753(in Chinese)
[9]	Wang L, Gao X W, Wang W, Wang Q. Order production scheduling method based on subspace clustering mixed model and time-section ant colony algorithm. Acta Automatica Sinica, 2014, 40(9):1991-1997(in Chinese)
[10]	Ferreira M R, De Carvalho F D A T. Kernel fuzzy c-means with automatic variable weighting. Fuzzy Sets and Systems, 2014, 237:1-46
[11]	Krinidis S, Chatzis V. A robust fuzzy local information c-means clustering algorithm. IEEE Transactions on Image Processing, 2010, 19(5):1328-1337
[12]	Krinidis S, Krinidis M. Generalised fuzzy local information c-means clustering algorithm. Electronics Letters, 2012, 48(23):1468-1470
[13]	Gong M G, Liang Y, Shi J, Ma W P, Ma J J. Fuzzy c-means clustering with local information and kernel metric for image segmentation. IEEE Transactions on Image Processing, 2013, 22(2):573-584
[14]	Chiranjeevi P, Sengupta S. Detection of moving objects using multichannel kernel fuzzy correlogram based background subtraction. IEEE Transactions on Cybernetics, 2014, 44(6):870-881
[15]	Alipour S, Shanbehzadeh J. Fast automatic medical image segmentation based on spatial kernel fuzzy c-means on level set method. Machine Vision and Applications, 2014, 25(6):1469-1488
[16]	Lu C H, Xiao S Q, Gu X F. Improving fuzzy c-means clustering algorithm based on a density-induced distance measure. The Journal of Engineering, 2014, 1(1):1-3
[17]	Qiu C Y, Xiao J, Han L, Naveed Iqbal M. Enhanced interval type-2 fuzzy c-means algorithm with improved initial center. Pattern Recognition Letters, 2014, 38:86-92
[18]	Candés E J, Li X D, Ma Y, Wright J. Robust principal component analysis. Journal of the ACM (JACM), 2011, 58(3):Article No. 11
[19]	Yang M S, Lai C Y, Lin C Y. A robust EM clustering algorithm for Gaussian mixture models. Pattern Recognition, 2012, 45(11):3950-3961
[20]	Elhamifar E, Vidal R. Robust classification using structured sparse representation. In:Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Providence, RI, USA:IEEE, 2011. 1873-1879
[21]	Xie X M, Wang C M, Zhang A J, Meng X F. A robust level set method based on local statistical information for noisy image segmentation. Optik-International Journal for Light and Electron Optics, 2014, 125(9):2199-2204
[22]	Filippone M, Camastra F, Masulli F, Rovetta S. A survey of kernel and spectral methods for clustering. Pattern Recognition, 2008, 41(1):176-190
[23]	Zhang J P, Chen F C, Li S M, Liu L X. Data stream clustering algorithm based on density and affinity propagation techniques. Acta Automatica Sinica, 2014, 40(2):277-288(in Chinese)
[24]	Kantardzic M. Data Mining:Concepts, Models, Methods, and Algorithms. Second Edition. New York:John Wiley & Sons, 2011. 249-259
[25]	Anderson M J, Ellingsen K E, McArdle B H. Multivariate dispersion as a measure of beta diversity. Ecology Letters, 2006, 9(6):683-693
[26]	Anderson M J, Santana-Garcon J. Measures of precision for dissimilarity-based multivariate analysis of ecological communities. Ecology Letters, 2015, 18(1):66-73
[27]	Cormen T H, Leiserson C E, Rivest R L, Stein C. Introduction to Algorithms. Cambridge:MIT Press, 2001. 1-7
[28]	Qian J B, Dong Y S. A clustering algorithm based on broad first searching neighbors. Journal of Southeast University (Natural Science Edition), 2004, 34(1):109-112(in Chinese)
[29]	Bezdek J C, Hathaway R J, Sabin M J, Tucker W T. Convergence theory for fuzzy c-means:counterexamples and repairs. IEEE Transactions on Systems, Man, and Cybernetics, 1987, 17(5):873-877
[30]	Shahriari H, Ahmadi O. Robust estimation of the mean vector for high-dimensional data set using robust clustering. Journal of Applied Statistics, 2015, 42(6):1183-1205
[31]	Kinoshita N, Endo Y. EM-based clustering algorithm for uncertain data. Knowledge and Systems Engineering, 2014, 245:69-81
[32]	Gao J, Wang S T. Fuzzy clustering algorithm with ranking features and identifying noise simultaneously. Acta Automatica Sinica, 2009, 35(2):145-153(in Chinese)

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Robust Dataset Classification Approach Based on Neighbor Searching and Kernel Fuzzy C-Means

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