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Double Layered Genetic Algorithm for Document Clustering

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Software Engineering, Business Continuity, and Education (ASEA 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 257))

Abstract

Genetic algorithm for document clustering(GC) shows good performance. However the genetic algorithm has problem of performance degradation by premature convergence phenomenon(PCP). In this paper, we propose double layered genetic algorithm for document clustering(DLGC) to solve this problem. The clustering algorithms including DLGC are tested and compared on Reuter-21578 data collection. The results show that our DLGC has the best performance among traditional clustering algorithms(K-means, Group Average Clustering) and GC in various experiments.

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References

  1. Foster, I., Kesselman, C.: Modern information retrieval. Addison-Wesley (1999)

    Google Scholar 

  2. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press (2008)

    Google Scholar 

  3. Croft, W.B., Metzler, D., Strohman, T.: Search Engines Information Retrieval in Practice. Addison Wesley (2009)

    Google Scholar 

  4. Maulik, U., Bandyopadhyay, S.: Genetic Algorithm-based Clustering Technique. Pattern Recognition 33(9), 1455–1465 (2000)

    Article  Google Scholar 

  5. Bandyopadhyay, S., Mauilk, U.: Nonparametric genetic clustering: Comparison of validity indices. IEEE Trans. System Man Cybern.-Part C Applications and Reviews 31, 120–125 (2001)

    Article  Google Scholar 

  6. Song, W., Park, S.C.: Genetic Algorithm-Based Text Clustering Technique. In: Jiao, L., Wang, L., Gao, X.-b., Liu, J., Wu, F. (eds.) ICNC 2006, Part I. LNCS, vol. 4221, pp. 779–782. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  7. Goldberg, D.E.: The Grid: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley (1989)

    Google Scholar 

  8. David, L.D.: Handbook of Genetic Algorithms. Van Nostrand Reinhold (1991)

    Google Scholar 

  9. Andre, J., Siarry, P., Dognon, T.: An improvement of the standard genetic algorithm fighting premature convergence in continuous optimization. Advances in Engineering Software 32(1), 49–60 (2001)

    Article  MATH  Google Scholar 

  10. Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. Presented at IEEE Trans. Evolutionary Computation, 82–102 (1999)

    Google Scholar 

  11. Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal.l Intell. 1, 224–227 (1979)

    Article  Google Scholar 

  12. Song, W., Park, S.C.: Genetic algorithm for text clustering based on latent semantic indexing. Presented at Computers & Mathematics with Applications, 1901–1907 (2009)

    Google Scholar 

  13. Selim, S.Z., Ismail, M.A.: K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality. IEEE Trans. Pattern Anal. Mach. Intell., 81–87 (1984)

    Google Scholar 

  14. Zhao, Y., Karypis, G., Fayyad, U.M.: Hierarchical Clustering Algorithms for Document Datasets. Data Min. Knowl. Discov. 10(2), 141–167 (2006)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Division of Electronics and Information Engineering, Jeonbuk National University, Jeonju, Jeonbuk, Republic of Korea

    Lim Cheon Choi, Jung Song Lee & Soon Cheol Park

Authors
  1. Lim Cheon Choi
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  2. Jung Song Lee
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  3. Soon Cheol Park
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Editor information

Editors and Affiliations

  1. Multimedia Engineering Department, Hannam University, 133 Ojeong-dong, Daeduk-gu, Daejeon, Korea

    Tai-hoon Kim

  2. The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, 43210-1275, Columbus, OH, USA

    Hojjat Adeli

  3. Catholic University of Daegu, South Korea

    Haeng-kon Kim

  4. Dept. of Computer Engineering, Mokwon University, 800, Doan-dong, Seo-gu, 302-729, Daejeon, Korea

    Heau-jo Kang

  5. Woosuk University, 565-701, Jeollabuk-do, Korea

    Kyung Jung Kim

  6. University of Michigan-Dearborn, Dearborn, MI, USA

    Akingbehin Kiumi

  7. School of Computing and Information Systems, University of Tasmania, Hobart, TAS, Australia

    Byeong-Ho Kang

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© 2011 Springer-Verlag Berlin Heidelberg

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Choi, L.C., Lee, J.S., Park, S.C. (2011). Double Layered Genetic Algorithm for Document Clustering. In: Kim, Th., et al. Software Engineering, Business Continuity, and Education. ASEA 2011. Communications in Computer and Information Science, vol 257. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27207-3_21

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  • DOI: https://doi.org/10.1007/978-3-642-27207-3_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27206-6

  • Online ISBN: 978-3-642-27207-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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