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An Agglomerate Chameleon Algorithm Based on the Tissue-Like P System

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Bio-Inspired Computing -- Theories and Applications (BIC-TA 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 562))

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Abstract

The Chameleon algorithm plays an important role in data mining and data analysis. Membrane computing, as a new kind of parallel biological computing model, can reduce the time complexity and improve the computational efficiency. In this study, an agglomerate Chameleon algorithm is proposed which generates the sub-clusters by the K-medoids algorithm method. Then, the agglomerate Chameleon algorithm based on the Tissue-like P system is constructed with all the rules being created. The time complexity of the proposed algorithm is decreased from \(O(K*(n-K)^{2}*C_n^K)_{}^{}\) to \(O(n*C_n^K)_{}^{}\) through the parallelism of the P system. Experimental results show that the proposed algorithm has low error rate and is appropriate for big cluster analysis. The proposed algorithm in this study is a new attempt in applications of membrane system and it provides a novel perspective of cluster analysis.

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References

  1. Han, J., Kambr, M.: Data Mining Concepts and Techniques. Elsevier Inc., USA (2012)

    Google Scholar 

  2. Che, D., Safran, M., Peng, Z.: From big data to big data mining: challenges, issues, and opportunities. In: Hong, B., Meng, X., Chen, L., Winiwarter, W., Song, W. (eds.) DASFAA Workshops 2013. LNCS, vol. 7827, pp. 1–15. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  3. Paun, G.: A quick introduction to membrane computing. J. Logic Algebraic Program. 79(1), 291–294 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  4. Freund, R., Oswald, M., Paun, G.: Catalytic and purely catalytic P systems and P automata: control mechanisms for obtaining computational completeness. Fundamenta Informaticae 136, 59–84 (2015)

    MathSciNet  MATH  Google Scholar 

  5. Freund, R., Kari, L., Oswald, M., Sosik, P.: Computationally universal P systems without priorities: two catalysts are sufficient. Theoret. Comput. Sci. 330(2), 251–266 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  6. Gheorghe, M., Krasnogor, N., Camara, M.: P systems applications to systems biology. Biosystems 91(3), 435–437 (2008)

    Article  Google Scholar 

  7. Romero-Campero, F.J., Prez-Jimnez, M.J.: Modelling gene expression control using P systems: the lac operon, a case study. Biosystems 91(3), 438–457 (2008)

    Article  Google Scholar 

  8. Enguix, G.B.: Preliminaries about some possible applications of P systems in linguistics. In: Păun, G., Rozenberg, G., Salomaa, A., Zandron, C. (eds.) WMC 2002. LNCS, vol. 2597, pp. 74–89. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  9. Enguix, G.B.: Unstable P systems: applications to linguistics. In: Mauri, G., Păun, G., Jesús Pérez-Jímenez, M., Rozenberg, G., Salomaa, A. (eds.) WMC 2004. LNCS, vol. 3365, pp. 190–209. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  10. Andrei, O., Ciobanu, G., Lucanu, D.: A rewriting logic framework for operational semantics of membrane systems. Theoret. Comput. Sci. 373(3), 163–181 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  11. Idowu, R.K., Maroosi, A., Muniyandi, R.C., Othman, Z.A.: An application of membrane computing to anomaly-based intrusion detection system. In: International Conference on Electrical Engineering and Informatics, vol. 11(1), pp. 585–592 (2013)

    Google Scholar 

  12. Daz-Pernil, D., Berciano, A., Pena-Cantillana, F., GutiRrez-Naranjo, M.A.: Segmenting images with gradient-based edge detection using membrane computing. Pattern Recogn. Lett. 34(8), 846–855 (2013)

    Article  Google Scholar 

  13. Peng, H., Wang, J., Prez-Jimnez, M.J., Riscos-Núñez, A.: The framework of P systems applied to solve optimal watermarking problem. Sig. Process. 101(4), 256–265 (2014)

    Article  Google Scholar 

  14. Paun, G., Rozenberg, G., Salomaa, A.: The Oxford Handbook of Membrane Computing. Oxford University Press, New York (2010)

    Book  MATH  Google Scholar 

  15. Paun, G., Paun, R.: Membrane computing and economics: numerical P systems. Fundamenta Informaticae (2006)

    Google Scholar 

  16. http://ppage.psystems.eu/index.php/Software

  17. Pavel, A., Arsene, O., Buiu, C.: Enzymatic numerical P systems - a new class of membrane computing systems. In: 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), pp. 1331–1336. IEEE (2010)

    Google Scholar 

  18. Barbuti, R., Maggiolo-Schettini, A., Milazzo, P., Pardini, G., Tesei, L.: Spatial P systems. Nat. Comput. 10(1), 3–16 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  19. Verlan, S.: Using the formal framework for P systems. In: Alhazov, A., Cojocaru, S., Gheorghe, M., Rogozhin, Y., Rozenberg, G., Salomaa, A. (eds.) CMC 2013. LNCS, vol. 8340, pp. 56–79. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  20. Gheorghe, M., Ipate, F.: A kernel P systems survey. In: Alhazov, A., Cojocaru, S., Gheorghe, M., Rogozhin, Y., Rozenberg, G., Salomaa, A. (eds.) CMC 2013. LNCS, vol. 8340, pp. 1–9. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  21. Gazdag, Z.: Solving SAT by P systems with active membranes in linear time in the number of variables. In: Alhazov, A., Cojocaru, S., Gheorghe, M., Rogozhin, Y., Rozenberg, G., Salomaa, A. (eds.) CMC 2013. LNCS, vol. 8340, pp. 189–205. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  22. Cienciala, L., Ciencialová, L., Langer, M.: Modelling of surface runoff using 2D P colonies. In: Alhazov, A., Cojocaru, S., Gheorghe, M., Rogozhin, Y., Rozenberg, G., Salomaa, A. (eds.) CMC 2013. LNCS, vol. 8340, pp. 101–116. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  23. Banu-Demergian, I.T., Stefanescu, G.: The geometric membrane structure of finite interactive systems scenarios. In: Membrane Computing, pp. 63–80. Springer, Heidelberg (2013)

    Google Scholar 

  24. Sun, J., Liu, X.Y.: Density-Based Clustering by P System with Active Membranes on Commodity Recommendation in E-commerce Websites. Wseas Org (2014)

    Google Scholar 

  25. Peng, H., Zhang, J., Jiang, Y., Huang, X., Wang, J.: DE-MC: a membrane clustering algorithm based on differential evolution mechanism. Rom. J. Inf. Sci. Technol. 17(1), 76–88 (2014)

    Google Scholar 

  26. Peng, H., Jiang, Y., Wang, J., Prez-Jimnez, M.J.: Membrane clustering algorithm with hybrid evolutionary mechanisms. J. Softw. 26(5), 1001–1012 (2015)

    MathSciNet  MATH  Google Scholar 

  27. Karypis, G., Han, E., Kumar, V.C.: A hierarchical clustering algorithm using dynamic modeling. IEEE Comput. (1999)

    Google Scholar 

  28. Zhang, G.X., Pan, L.Q.: A survey of membrane computing as a new branch of natural computing. Chin. J. Comput. 33(2), 208–214 (2010)

    Article  Google Scholar 

  29. Paun, G.: A quick introduction to membrane computing. J. Logic Algebraic Program. 287(1), 73–100 (1999)

    Google Scholar 

  30. Paun, G., Salomaa, A.: Membrane Computing. Oxford University Press, New York (2008)

    MATH  Google Scholar 

  31. Martn-Vide, C., Paun, G., Pazos, J., Rodrguez-Patn, A.: Tissue P systems. Theoret. Comput. Sci. 296(2), 295–326 (2001)

    Article  MathSciNet  Google Scholar 

  32. Han, J., Kambr, M.: Data Mining Concepts and Techniques, Chap. 5, pp. 237–238. Elsevier Inc., USA (2012)

    Google Scholar 

  33. Paun, G., Rozenberg, G.: A guide to membrane computing. Theor. Comput. Sci. 287(1), 73–100 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  34. http://archive.ics.uci.edu/ml

  35. Wei, Y., Xiangting, L.: Statistics and consultation, the empirical comparison between the SOM algorithm and the K-means algorithm, pp. 22–23, March 2009

    Google Scholar 

  36. Cottrell, M., Fort, J.-C., Pages, G.: Theoretical aspects of the SOM algorithm. Neurocomputing 21(1), 119–138 (1998)

    Article  MATH  Google Scholar 

  37. Tokunaga, K., Furukawa, T.: Modular network SOM. Neural Netw. 22(1), 82–90 (2009)

    Article  Google Scholar 

  38. Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (2001)

    Book  MATH  Google Scholar 

  39. Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990)

    Article  Google Scholar 

  40. Kangas, J.A., Kohonen, T.K., Laaksonen, J.T.: Variants of self-organizing maps. IEEE Trans. Neural Netw. 1(1), 93–99 (1990)

    Article  Google Scholar 

  41. MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297. University of California Press, Berkeley (1967)

    Google Scholar 

  42. Cox, D.R.: Note on grouping. J. Am. Stat. Assoc. 52(280), 543–547 (1957)

    Article  MATH  Google Scholar 

  43. Fisher, W.D.: On grouping for maximum homogeneity. J. Am. Stat. Assoc. 53(284), 789–798 (1958)

    Article  MathSciNet  MATH  Google Scholar 

  44. Sebestyen, G.S.: Decision Making Process in Pattern Recognition. Macmillan, New York (1962)

    MATH  Google Scholar 

Download references

Acknowledgments

This study is supported by Natural Science Foundation of China (No. 61170038), Natural Science Foundation of Shandong Province, China (No. ZR2011FM001), Humanities and Social Sciences Project of Ministry of Education, China (No. 12YJA630152), Social Science Fund of Shandong Province (No. 11CGLJ22), Science-Technology Program of the Higher Education Institutions of Shandong Province, China (No. J12LN22), Science-Technology Program of the Higher Education Institutions of Shandong Province (No. J12LN65), Research Award Foundation for Outstanding Young Scientists of Shandong Province (No. BS2012DX041).

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Authors and Affiliations

  1. School of Management Science and Engineering, Shandong Normal University, Jinan, 250014, China

    Yuzhen Zhao, Xiyu Liu & Wenping Wang

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  1. Yuzhen Zhao
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  2. Xiyu Liu
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  3. Wenping Wang
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Corresponding author

Correspondence to Xiyu Liu .

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Editors and Affiliations

  1. Xidian University, Xi'an, China

    Maoguo Gong

  2. Huazhong Univ. of Science and Technology, Wuhan, China

    Pan Linqiang

  3. University of Petroleum, Qingdao, China

    Song Tao

  4. University of Science and Technology of China, Hefei, China

    Ke Tang

  5. Anhui University, Hefei, China

    Xingyi Zhang

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Zhao, Y., Liu, X., Wang, W. (2015). An Agglomerate Chameleon Algorithm Based on the Tissue-Like P System. In: Gong, M., Linqiang, P., Tao, S., Tang, K., Zhang, X. (eds) Bio-Inspired Computing -- Theories and Applications. BIC-TA 2015. Communications in Computer and Information Science, vol 562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49014-3_63

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  • DOI: https://doi.org/10.1007/978-3-662-49014-3_63

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49013-6

  • Online ISBN: 978-3-662-49014-3

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