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Home > Archive>Volume 26, Issue 5, 2015 >1001-1012. DOI:10.13328/j.cnki.jos.004657
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Membrane Clustering Algorithm with Hybrid Evolutionary Mechanisms
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    Abstract:

    Membrane computing, known as P systems or membrane systems, is a novel class of distributed and parallel computing models. This paper proposes a membrane clustering algorithm using hybrid evolutionary mechanisms to address data clustering problem. It uses a tissue P system consisting of three cells to find the optimal cluster centers for a data set to be clustered. Its object is used to express candidate cluster centers, and the three cells use three different evolutionary mechanisms: genetic operators, velocity-position model and differential evolution mechanism. Particularly, the velocity-position model and differential evolution mechanism used in the process are the improved versions proposed in this paper according to the special membrane structure and communication mechanism. The hybrid evolutionary mechanisms can enhance the diversity of objects in the system and improve the convergence performance. Under the control of the hybrid evolutionary mechanisms and communication mechanism, the membrane clustering algorithm can determine a good partition for a data set. The proposed membrane clustering algorithm is evaluated on three artificial data sets and five real-life data sets and compared with k-means and several evolutionary clustering algorithms. Statistical significance tests have been performed to establish the superiority of the proposed membrane clustering algorithm.

    Reference
    [1] Gan G, Ma C, Wu J. Data Clustering: Theory, Algorithms, and Applications. SIAM, 2007.
    [2] Xu R, Wunsch D. Survey of clustering algorithm. IEEE Trans. on Neural Networks, 2005,16(3):645-678. [doi: 10.1109/TNN.2005. 845141]
    [3] Jain AK. Data clustering: 50 years beyond k-means. Pattern Recognition Letters, 2010,31(8):651-666. [doi: 10.1016/j.patrec.2009. 09.011]
    [4] Everitt B, Landau S, Leese M. Cluster Analysis. London: Arnold, 2001.
    [5] Saha S, Bandyopadhyay S. A symmetry based multiobjective clustering technique for automatic evolution of clusters. Pattern Recognition, 2010,43(3):738-751. [doi: 10.1016/j.patcog.2009.07.004]
    [6] Das S, Sil S. Kernel-Induced fuzzy clustering of image pixels with an improved different evolution algorithm. Information Sciences, 2010,180(8):1237-1256. [doi: 10.1016/j.ins.2009.11.041]
    [7] Naldi MC, Campello RJGB, Hruschka ER, Carvalho ACPLF. Efficiency issues of evolutionary k-means. Applied Soft Computing, 2011,11(8):1938-1952. [doi: 10.1016/j.asoc.2010.06.010]
    [8] Kanungo T, Mount D, Netanyahu NS, Piatko C, Silverman R, Wu A. An efficient k-means clustering algorithm: Analysis and implementation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2002,24(7):881-892. [doi: 10.1109/TPAMI.2002. 1017616]
    [9] Wu X, Kumar V. The Top Ten Algorithms in Data Mining. Chapman and Hall/CRC, 2009.
    [10] Murthy CA, Chowdhury N. In search of optimal clusters using genetic algorithms. Parttern Recognition Letters, 1996,17(8):825-832. [doi: 10.1016/0167-8655(96)00043-8]
    [11] Maulik U, Bandyopadhyay S. Genetic algorithm based clustering technique. Pattern Recognition, 2000,33(9):1455-1465. [doi: 10.1016/S0031-3203(99)00137-5]
    [12] Bandyopdhyay S, Saha S. GAPS: A clustering method using a new point symmetry-based distance measure. Pattern Recognition, 2007,40(12):3430-3451. [doi: 10.1016/j.patcog.2007.03.026]
    [13] Bandyopdhyay S. Genetic algorithms for clustering and fuzzy clustering. Data Mining and Knowledge Discovery, 2011,1(6): 524-531. [doi: 10.1002/widm.47]
    [14] Chang D, Zhang X, Zheng C. A genetic algorithm with gene rearrangment for k-means clustering. Pattern Recognition, 2009,42(7): 1210-1222. [doi: 10.1016/j.patcog.2008.11.006]
    [15] Nguyen CD, Cios KJ. GAKREM: A novel hybrid clustering algorithm. Information Sciences, 2008,178(22):4205-4227. [doi: 10. 1016/j.ins.2008.07.016]
    [16] Laszlo M, Mukherjee S. A genetic algorithm that exchanges neighboring centers for k-means clustering. Pattern Recognition Latters, 2007,28(16):2359-2366. [doi: 10.1016/j.patrec.2007.08.006]
    [17] Kao YT, Zahara E, Kao IW. A hybridized approach to data clustering. Expert Systems with Applications, 2008,34(3): 1754-1762. [doi: 10.1016/j.eswa.2007.01.028]
    [18] Shelokar PS, Jayaraman VK, Kulkarni BD. An ant colony approach for clustering. Analytica Chimica Acta, 2004,509(2): 187-195. [doi: 10.1016/j.aca.2003.12.032]
    [19] Niknam T, Amiri B. An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis. Applied Soft Computing, 2010,10(1):183-197. [doi: 10.1016/j.asoc.2009.07.001]
    [20] Păun G. Computing with membranes. Journal of Computer and System Sciences, 2000,61(1):108-143. [doi: 10.1006/jcss.1999. 1693]
    [21] Păun G, Rozenberg G, Salomaa A. The Oxford Handbook of Membrane Computing. New York: Oxford University Press, 2010.
    [22] Freund R, Păun G, Pérez-Jiménez MJ. Tissue-Like P systems with channel-states. Theoretical Computer Science, 2005,330(1): 101-116. [doi: 10.1016/j.tcs.2004.09.013]
    [23] Ionescu M, Păun G, Yokomori T. Spiking neural P systems. Fundameta Informaticae, 2006,71(2-3):279-308.
    [24] Păun G, Pérez-Jiménez MJ. Membrane computing: Brief introduction, recent results and applications. BioSystems, 2006,85(1): 11-22. [doi: 10.1016/j.biosystems.2006.02.001]
    [25] Wang J, Zhou L, Peng H, Zhang GX. An extended spiking neural P system for fuzzy knowledge representation. Int'l Journal of Innovative Computing, Information and Control, 2011,7(7A):3709-3724.
    [26] Peng H, Wang J, Pérez-Jiménez MJ, Wang H, Shao J, Wang T. Fuzzy reasoning spiking neural P system for fault diagnosis. Information Sciences, 2013,235:106-116. [doi: 10.1016/j.ins.2012.07.015]
    [27] Wang J, Shi P, Peng H, Pérez-Jiménez MJ, Wang T. Weighted fuzzy spiking neural P systems. IEEE Trans. on Fuzzy Systems, 2013,21(2):209-220. [doi: 10.1109/TFUZZ.2012.2208974]
    [28] Peng H, Wang J, Pérez-Jiménez MJ, Shi P. A novel image thresholding method based on membrane computing and fuzzy entropy. Journal of Intelligent & Fuzzy Systems, 2013,24(2):229-237. [doi: 10.3233/IFS-2012-0549]
    [29] Davis L. Handbook of Genetic Algorithms. Van Nostrand Reinhold, 1991.
    [30] Kennedy J, Eberhart R. Particle swarm optimization. In: Proc. of the IEEE Int'l Conf. on Neural Network, Vol.4. Piscataway, 1995. 1942-1948. [doi: 10.1109/ICNN.1995.488968]
    [31] Price K, Storn R, Lampinen J. Different Evolution—A Practical Approach to Global Optimization. Berlin: Springer-Verlag, 2005. [doi: 10.1007/3-540-31306-0]
    [32] Zhang GX, Cheng JX, Gheorghe M, Meng Q. A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems. Applied Soft Computing, 2013,13(3):1528-1542. [doi: 10. 1016/j.asoc.2012.05.032]
    [33] Colomer AM, Margalida A, Pérez-Jiménez MJ. Population dynamics P systems (PDP) models: A standarized protocol for describing and applying novel bio-inspired computing tools. Plos One, 2013,8(4):1-13. [doi: 10.1371/journal.pone.0060698]
    [34] Frisco P, Gheorghe M, Pérez-Jiménez MJ. Applications of Membrane Computing in Systems and Synthetic Biology. Springer- Verlag, 2013. [doi: 10.1007/978-3-319-03191-0]
    [35] UCI datasets. http://www.ics.uci.edu/~mlearn/MLRepository.html
    [36] Chou CH, Su MC, Lai E. A new cluster validity measure and its application to image compression. Pattern Analysis and Applications, 2004,7(2):205-220. [doi: 10.1007/s10044-004-0218-1]
    [37] Hollander M, Wolfe DA. Nonparametric Statistical Methods. 2nd ed., 1999.
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彭宏,蒋洋,王军,Mario J. P&#;REZ-JIM&#;NEZ.一种带混合进化机制的膜聚类算法.软件学报,2015,26(5):1001-1012

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History
  • Received:October 28,2013
  • Revised:May 21,2014
  • Online: August 22,2014
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