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A Genetic Programming Approach to Data Clustering

  • Conference paper
Multimedia, Computer Graphics and Broadcasting (MulGraB 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 263))

Abstract

This paper presents a genetic programming (GP) to data clustering. The aim is to accurately classify a set of input data into their genuine clusters. The idea lies in discovering a mathematical function on clustering regularities and then utilize the rule to make a correct decision on the entities of each cluster. To this end, GP is incorporated into the clustering procedures. Each individual is represented by a parsing tree on the program set. Fitness function evaluates the quality of clustering with regard to similarity criteria. Crossover exchanges sub-trees between parental candidates in a positionally independent fashion. Mutation introduces (in part) a new sub-tree with a low probability. The variation operators (i.e., crossover, mutation) offer an effective search capability to obtain the improved quality of solution and the enhanced speed of convergence. Experimental results demonstrate that the proposed approach outperforms a well-known reference.

This research was supported by the MKE, Korea under the ITRC NIPA-2011-(C1090-1121-0008).

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References

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Author information

Authors and Affiliations

  1. School of Information & Communication Engineering, Sungkyunkwan University, Suwon, 440-746, Korea

    Chang Wook Ahn & Sanghoun Oh

  2. Department of Medical Administration, Busan College of Information Technology, Korea

    Moonyoung Oh

Authors
  1. Chang Wook Ahn
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  2. Sanghoun Oh
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  3. Moonyoung Oh
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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. Department of Computer and Information Science, University of Michigan – Dearborn, 4901 Evergreen Road, 48128, Dearborn, MI, USA

    William I. Grosky

  4. Florida International University, EC 2910, 10555 W. Flagler Street, 33174, Miami, FL, USA

    Niki Pissinou

  5. National Taipei University of Education, No.134, Sec. 2, Heping E. Rd., Da-an District, 106, Taipei City, Taiwan (R.O.C.)

    Timothy K. Shih

  6. Michigan State University, 48824-1226, East Lansing, MI, USA

    Edward J. Rothwell

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

    Byeong-Ho Kang

  8. Dept of IT, Hansei University, 604-5, Dangjung-Dong, Gunpo-Si, Gyunggi-Do, Korea

    Seung-Jung Shin

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© 2011 Springer-Verlag Berlin Heidelberg

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Ahn, C.W., Oh, S., Oh, M. (2011). A Genetic Programming Approach to Data Clustering. In: Kim, Th., et al. Multimedia, Computer Graphics and Broadcasting. MulGraB 2011. Communications in Computer and Information Science, vol 263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27186-1_15

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  • DOI: https://doi.org/10.1007/978-3-642-27186-1_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27185-4

  • Online ISBN: 978-3-642-27186-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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