Abstract
When the data set is massive and dense, it can often be convenient that clustering all effective grid cells which contain many points. This study firstly divides the data points into different grid cells. Then, the study proposed a specific P system to compute the improved K-medoids. Clustering Algorithm based on a grid cell graph and extended the application of membrane computing. The study improve the K-medoids algorithm by selecting the k initial centers based on the gravitation between the effective grid cells which can greatly improve the quality of clustering. The study make the gravitation between two grid cells as the similarity. As we all known, the P system has the advantage of high parallelism and lower computational time complexity. This specific P system also can handle the big data based on the level of grid cells.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Păun, G.: Membrane computing. In: Lingas, A., Nilsson, B.J. (eds.) FCT 2003. LNCS, vol. 2751, pp. 284–295. Springer, Heidelberg (2003)
Chen, X.: Clustering based on a near neighbor graph and a grid cell graph. J. Intell. Inf. Syst. 40(3), 529–554 (2013)
Ester, M., Kriedel, H., Sander, J., et al.: A density-based algorithm for discovering clustering clusters in large spatial database with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, USA, pp. 226–231 (1996)
Mohammed, M.: An efficient density based improved K- medoids clustering algorithm. Int. J. Comput. Distrib. Syst. (2011)
Li, Q., Liu, X.: A K-medoids clustering algorithm with initial centers optimized by a P system. In: Zu, Q., Hu, B., Gu, N., Seng, S. (eds.) HCC 2014. LNCS, vol. 8944, pp. 488–500. Springer, Heidelberg (2015)
Paun, G.: A quick introduction to membrane computing. J. Logic Algebraic Program. 79, 291–294 (2010)
Han, L., Xiang, L., Liu, X., Luan, J.: The K-medoids algorithm with initial centers optimized based on a P system. Inf. Comput. Sci. 11(6), 132–144 (2014)
Pérez Jiménez, M.J., Romero Campero, F.J.: Attacking the common algorithmic problem by recognizer P systems. In: Margenstern, M. (ed.) MCU 2004. LNCS, vol. 3354, pp. 304–315. Springer, Heidelberg (2005)
Paun, G.: Computing with membranes. J. Comput. Syst. Sci. 61, 108–143 (2000)
Zhang, G.X., Pan, L.Q.: A survey of membrane computing as a new branch of natural computing. Chin. J. Comput. 33, 208–214 (2010)
Marc, G.A., Daniel, M., Alfonso, R.P., Petr, S.: A P system and a constructive membrane-inspired DNA algorithm for solving the maximum clique problem. BioSystems 90, 687–697 (2007)
Paun, G., Rozenberg, G., Salomaa, A.: The Oxford Handbook of Membrane Computing. Oxford University Press, New York (2010)
Paun, G., Paun, R.: Membrane computing and economics: Numerical P systems. Fundamenta Informaticae. J. 73(1), 213–227 (2006)
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)
Jie, S., Liu, X.Y.: Density-based clustering by P system with active membranes on commodity recommendation in E-commerce websites. WSEAS Trans. Comput. 13, 20–33 (2014)
Freund, R., Oswald, M., Paun, G.: Catalytic and purely catalytic P system and P automata: control mechnisms for obtaining computational completeness. In: 14th International Conference, CMC14, pp. 317–320 (2013)
Acknowledgement
Project supported by National Natural Science Foundation of China (61170038,61472231), Jinan City independent innovation plan project in College and Universities, China (201401202), Ministry of education of Humanities and social science research project, China (12YJA630152), Social Science Fund Project of Shandong Province, China (11CGLJ22), outstanding youth scientist foundation project of Shandong Province, China (BS2013DX037).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Sun, W., Xiang, L., Liu, X., Zhao, D. (2016). An Improved K-medoids Clustering Algorithm Based on a Grid Cell Graph Realized by the P System. In: Zu, Q., Hu, B. (eds) Human Centered Computing. HCC 2016. Lecture Notes in Computer Science(), vol 9567. Springer, Cham. https://doi.org/10.1007/978-3-319-31854-7_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-31854-7_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31853-0
Online ISBN: 978-3-319-31854-7
eBook Packages: Computer ScienceComputer Science (R0)