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A New Encoding Method for Graph Clustering Problem

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High-Performance Computing and Big Data Analysis (TopHPC 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 891))

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Abstract

Clustering is used as an important technique to extract patterns from big data in various fields. Graph clustering as a subset of clustering has a lot of practical applications. Due to the NP-hardness of the graph clustering problem, many evolutionary algorithms, particularly the genetic algorithm have been presented. One of the most effective operators on the performance of the genetic algorithm is how to represent the solutions of a problem (i.e. encoding). The number of possible partitions of a graph is equal to Bell Number. In the literature, three encoding methods have been presented for graph clustering problem. The number of partitions that these encodings can generate is more than the Bell Number; which indicates that these methods generate a large number of same and iterative solutions which makes the speed of obtaining the solution unacceptable and leads to this fact that the good space search encounters a problem. To overcome this drawback, in this paper we present a new encoding method for graph clustering problem where the number of the generated solutions by this encoding is exactly equal to the Bell numbers. The initial results of our experiments represent that the quality of the obtained solutions by the new encoding is promising.

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References

  1. Mahdavi, M., Chehreghani, M.H., Abolhassani, H., Forsati, R.: Novel meta-heuristic algorithms for clustering web documents. Appl. Math. Comput. 201(1–2), 441–451 (2008)

    MathSciNet  MATH  Google Scholar 

  2. Pizzuti, C., Rombo, S.E.: Algorithms and tools for protein-protein interaction networks clustering, with a special focus on population-based stochastic methods. Bioinformatics 30(10), 1343–1352 (2014)

    Article  Google Scholar 

  3. Said, A., Abbasi, R.A., Maqbool, O., Daud, A., Aljohani, N.R.: CC-GA: a clustering coefficient based genetic algorithm for detecting communities in social networks. Appl. Soft Comput. 63, 59–70 (2018)

    Article  Google Scholar 

  4. Krapivin, M., Marchese, M., Casati, F.: Exploring and understanding scientific metrics in citation networks. In: Zhou, J. (ed.) Complex 2009. LNICST, vol. 5, pp. 1550–1563. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02469-6_35

    Chapter  Google Scholar 

  5. Tumminello, M., Lillo, F., Piilo, J., Mantegna, R.N.: Identification of clusters of investors from their real trading activity in a financial market. New J. Phys. 14(1), 013041 (2012)

    Article  Google Scholar 

  6. Beck, F., Diehl, S.: On the impact of software evolution on software clustering. Empir. Softw. Eng. 18(5), 970–1004 (2013)

    Article  Google Scholar 

  7. Duncan, B., Peele, R.: Bell and Stirling numbers for graphs. J. Integer Seq. 12(09.7), 1 (2009)

    MathSciNet  MATH  Google Scholar 

  8. Schulz, C.: Graph partitioning and graph clustering in theory and practice. Institute for Theoretical Informatics Karlsruhe Institute of Technology (KIT), pp. 24–187, 20 May 2016

    Google Scholar 

  9. Palmer, C.C., Kershenbaum, A.: Representing trees in genetic algorithms. IBM Thomas J. Watson Research Division (1994)

    Google Scholar 

  10. Mitchell, B.S., Mancoridis, S.: On the automatic modularization of software systems using the bunch tool. IEEE Trans. Softw. Eng. 32(3), 193–208 (2006)

    Article  Google Scholar 

  11. Praditwong, K., Harman, M., Yao, X.: Software module clustering as a multi-objective search problem. IEEE Trans. Softw. Eng. 37(2), 264–282 (2011)

    Article  Google Scholar 

  12. Parsa, S., Bushehrian, O.: A new encoding scheme and a framework to investigate genetic clustering algorithms. J. Res. Pract. Inf. Technol. 37(1), 127 (2005)

    MATH  Google Scholar 

  13. Isazadeh, A., Izadkhah, H., Elgedawy, I.: Source Code Modularization: Theory and Techniques. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-319-63346-6

    Book  Google Scholar 

  14. Tajgardan, M., Izadkhah, H., Lotfi, S.: Software systems clustering using estimation of distribution approach. J. Appl. Comput. Sci. Methods 8(2), 99–113 (2016)

    Article  Google Scholar 

  15. Izadkhah, H., Elgedawy, I., Isazadeh, A.: E-CDGM: an evolutionary call-dependency graph modularization approach for software systems. Cybern. Inf. Technol. 16(3), 70–90 (2016)

    Google Scholar 

  16. Mitchell, B.S.: A heuristic search approach to solving the software clustering problem. Ph.D. theses. Drexel University (2002)

    Google Scholar 

  17. Mahdavi, K.: A clustering genetic algorithm for software modularisation with a multiple hill climbing approach. Diss, Brunel University (2005)

    Google Scholar 

  18. Huang, J., Liu, J.: A similarity-based modularization quality measure for software module clustering problems. Inf. Sci. 342, 96–110 (2016)

    Article  Google Scholar 

  19. Rajapati, A., Chhabra, J.K.: A particle swarm optimization-based heuristic for software module clustering problem. Arab. J. Sci. Eng. 43(12), 7083–7094 (2018)

    Article  Google Scholar 

  20. Chhabra, J.K.: Harmony search based remodularization for object-oriented software systems. Comput. Lang. Syst. Struct. 47, 153–169 (2017)

    Google Scholar 

  21. Chhabra, J.K.: Many-objective artificial bee colony algorithm for large-scale software module clustering problem. Soft Comput. 22(19), 6341–6361 (2018)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. University College of Daneshvaran, Tabriz, Iran

    Amir Hossein Farajpour Tabrizi

  2. Department of Computer Science, University of Tabriz, Tabriz, Iran

    Habib Izadkhah

Authors
  1. Amir Hossein Farajpour Tabrizi
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  2. Habib Izadkhah
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Corresponding author

Correspondence to Amir Hossein Farajpour Tabrizi .

Editor information

Editors and Affiliations

  1. University of Calabria, Rende, Italy

    Lucio Grandinetti

  2. Kharazmi University, Tehran, Iran

    Seyedeh Leili Mirtaheri

  3. University of Calabria, Rende, Italy

    Reza Shahbazian

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Tabrizi, A.H.F., Izadkhah, H. (2019). A New Encoding Method for Graph Clustering Problem. In: Grandinetti, L., Mirtaheri, S., Shahbazian, R. (eds) High-Performance Computing and Big Data Analysis. TopHPC 2019. Communications in Computer and Information Science, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-33495-6_26

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  • DOI: https://doi.org/10.1007/978-3-030-33495-6_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33494-9

  • Online ISBN: 978-3-030-33495-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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