Hierarchical Clustering by a P System with Chained Rules

  • Jie Sun
  • Xiyu Liu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 269)


Membrane computing has been applied in broad fields such as Biological modeling, NPC problems and combinatorial problems. It has great parallelism and non-determinacy. In this paper, a new variety of P system with chained rules is first applied to the problem of the hierarchical clustering. This new model of P system is designed to solve hierarchical clustering of individuals with nonnegative integer variables. Through performance analysis, this new model of P system can reduce the time complexity of clustering process comparing with the classical clustering algorithm. And it is proved to be more concise and explicit to realize agglomerative hierarchical clustering algorithm by example verification. This is a great improvement in applications of membrane computing.


Hierarchical clustering P system Chained rules 



This work was supported by the 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, China (No.11CGLJ22), Science-Technology Program of the Higher Education Institutions of Shandong Province, China (No.J12LN22).


  1. 1.
    Han J, Kambr M (2005) Data mining concepts and techniques, 2nd edn. Morgan Kaufmann, San FranciscoGoogle Scholar
  2. 2.
    Paun G, Rozenberg G, Salomaa A (2010) Membrane computing. Oxford University Press, New YorkGoogle Scholar
  3. 3.
    Cardona M, Colomer AM, Pérez-Jiménez MJ et al (1999) Hierarchical clustering with membrane computing. Comput Inform 27:497–513Google Scholar
  4. 4.
    Jia ZW, Cui J, Yu HJ (2009) Graph-clustering method based on the dissimilarity. J Shanxi Agric Univ 29(3):284–288Google Scholar
  5. 5.
    Sburlan D (2012) P systems with chained rules. Lect Notes Comput Sci 7186:359–370CrossRefMathSciNetGoogle Scholar
  6. 6.
    Zhao YZ, Liu XY, Qu JH (2012) The k-medoids clustering algorithm by a class of P system. J Inf Comput Sci 9(18):5777–5790Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.School of Management Science and EngineeringShandong Normal UniversityJinanChina

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