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A Novel Genetic Algorithm Based k-means Algorithm for Cluster Analysis

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The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018) (AMLTA 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 723))

Abstract

This paper proposed a novel genetic algorithm (GA) based k-means algorithm to perform cluster analysis. In the proposed approach, the population of GA is initialized by k-means algorithm. Then, the GA operators are applied to generate a new population. In addition, new mutation is proposed depending on the extreme points of clustering. The proposed approach is applied on a set of test problems. The results proved the superiority of the new methodology to perform cluster analysis well.

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References

  1. Everitt, B.S., Landau, S., Leese, M., Stahl, D.: Cluster Analysis, 5th edn. John Wiley & Sons Ltd., Chichester (2011)

    Book  MATH  Google Scholar 

  2. Karunakar Jureedi, N.V.V., Rosalina, K.M., Prema Kumar, N.: Clustering analysis and its application in electrical distribution system. Int. J. Electr. Electron. Comput. Syst. 1, 130–136 (2013)

    Google Scholar 

  3. Nithiyananthan, K.: Cluster analysis based fault identification data mining models for 3 phase power systems. Int. J. Innov. Sci. Res. 24, 285–292 (2016)

    Google Scholar 

  4. Singh, K., Malik, D., Sharma, N.: Evolving limitations in K-means algorithm in data mining and their removal. IJCEM Int. J. Comput. Eng. Manag. 12, 105–109 (2011)

    Google Scholar 

  5. Al Malki, A., Rizk, M.M., El-Shorbagy, M.A., Mousa, A.A.: Hybrid genetic algorithm with K-means for clustering problems. Open J. Optim. 5, 71–83 (2016)

    Article  Google Scholar 

  6. Maulik, U., Bandyopadhyay, S.: Genetic algorithm-based clustering technique. Pattern Recogn. 33, 1455–1465 (2000)

    Article  Google Scholar 

  7. El-Tarabily, M., Abdel-Kader, R., Marie, M.: A PSO-based subtractive data clustering algorithm. Int. J. Res. Comput. Sci. 3, 1–9 (2013)

    Article  Google Scholar 

  8. Liu, X., Guangdong, G., Fu, H.: An effective clustering algorithm with ant colony. J. Comput. 5, 598–605 (2010)

    Google Scholar 

  9. Wang, Y., Chen, L.: Multi-view fuzzy clustering with minimax optimization for effective clustering of data from multiple sources. Expert Syst. Appl. 1, 1–10 (2016)

    Article  Google Scholar 

  10. Ranjbar, M., Mosavi, M.R.: Simulated annealing clustering for optimum GPS satellite selection. Int. J. Comput. Sci. 9, 101–104 (2012)

    Google Scholar 

  11. Li, H., Chen, X., Wei, K.: An improved pigeon-inspired optimization for clustering analysis problems. Int. J. Comput. Intell. Appl. 16, 1–21 (2017)

    Article  Google Scholar 

  12. Chen, X., Zhou, Y., Luo, Q.: A hybrid monkey search algorithm for clustering analysis, 1–17 (2014)

    Google Scholar 

  13. Filho, J.R., Treleaven, P.C., Alippi, C.: Genetic algorithm programming environments. IEEE Comput. 27, 28–43 (1994)

    Article  Google Scholar 

  14. Alabsi, F., Naoum, R.: Comparison of selection methods and crossover operations using steady state genetic based intrusion detection system. J. Emerg. Trends Comput. Inf. Sci. 3, 1053–1058 (2012)

    Google Scholar 

  15. Mousa, A.A., El-Shorbagy, M.A.: Identifying a satisfactory operation point for fuzzy multiobjective environmental/economic dispatch problem. Am. J. Math. Comput. Model. 1, 1–14 (2016)

    Google Scholar 

  16. Farag, M.A., El-Shorbagy, M.A., El-Desoky, I.M., El-Sawy, A.A., Mousa, A.A.: Genetic algorithm based on k-means-clustering technique for multi-objective resource allocation problems. Br. J. Appl. Sci. Technol. 8, 80–96 (2015)

    Article  Google Scholar 

  17. Franti, P., Kivijarvi, J.: Randomised local search algorithm for the clustering problem. Pattern Anal. Appl. 3, 358–369 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  18. Mousa, A.A., El-Shorbagy, M.A., Farag, M.A.: K-means-clustering based evolutionary algorithm for multi-objective resource allocation problems. Appl. Math. Inf. Sci. 11, 1–12 (2017)

    Article  Google Scholar 

  19. Farag, M.A., El-Shorbagy, M.A., El-Desoky, I.M., Mousa, A.A., El-Sawy, A.A.: Binary-real coded genetic algorithm based k-Means clustering for unit commitment problem. Appl. Math. 6, 1873–1890 (2015)

    Article  Google Scholar 

  20. El-Desoky, I.M., Nasr, S.N., Hendawy, Z.M., Mousa, A.A., El-Shorbagy, M.A.: A hybrid genetic algorithm for job shop scheduling problems. Int. J. Adv. Eng. Technol. Comput. Sci. 3, 6–17 (2016)

    Google Scholar 

  21. Fränti, P., Virmajoki, O.: Iterative shrinking method for clustering problems. Pattern Recogn. 39, 761–765 (2006)

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Basic Engineering Science, Faculty of Engineering, Menoufia University, Shebin El-Kom, Egypt

    M. A. El-Shorbagy, A. Y. Ayoub, I. M. El-Desoky & A. A. Mousa

  2. Mathematics and Statistics Department, Faculty of Science, Taif University, Taif, Kingdom of Saudi Arabia

    A. A. Mousa

Authors
  1. M. A. El-Shorbagy
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  2. A. Y. Ayoub
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  3. I. M. El-Desoky
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  4. A. A. Mousa
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Corresponding author

Correspondence to M. A. El-Shorbagy .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Information Technology Department, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Computers and Information Sciences, Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

  3. Faculty of Computers and Information, Department of Computer Science and Engineering, Mansoura University, Dakahlia, Egypt

    Mohamed Elhoseny

  4. Arab Academy for Science, Technology and Maritime Transport (AASTMT), Dokki, Egypt

    Mohamed Mostafa

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El-Shorbagy, M.A., Ayoub, A.Y., El-Desoky, I.M., Mousa, A.A. (2018). A Novel Genetic Algorithm Based k-means Algorithm for Cluster Analysis. In: Hassanien, A., Tolba, M., Elhoseny, M., Mostafa, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_10

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  • DOI: https://doi.org/10.1007/978-3-319-74690-6_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74689-0

  • Online ISBN: 978-3-319-74690-6

  • eBook Packages: EngineeringEngineering (R0)

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