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Clustering Problem Using Adaptive Genetic Algorithm

  • Conference paper
Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3612))

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Abstract

Clustering is very important to data analysis and data minig. The K-Means algorithm, one of the partitional clustering approaches, is an iterative clustering technique that has been applied to many practical clustering problems successfully. However, the K-Means algorithm suffers from several drawbacks. In this paper, an adaptive genetic algorithm be present , it solve disadvantages of K-Means by combine parallel genetic algorithm, evolving flow and adaptive. Experimental results show that the adaptive genetic algorithm have advantages over traditional Clustering algorithm.

The Project Supported by Zhejiang Provincial Science Foundation of China (601081) and Key Lab of Information Processes of Jiangsu Province(Suzhou University).

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Author information

Authors and Affiliations

  1. Zhejiang University of Technology, 310032, Hangzhou, Zhejiang, China

    Qingzhan Chen, Yungang Lai, Wenxiu He & Keji Mao

  2. Heifei University of Technology, 230009, Hefei, Anhui, China

    Qingzhan Chen & Jianghong Han

Authors
  1. Qingzhan Chen
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  2. Jianghong Han
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  3. Yungang Lai
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  4. Wenxiu He
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  5. Keji Mao
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Chen, Q., Han, J., Lai, Y., He, W., Mao, K. (2005). Clustering Problem Using Adaptive Genetic Algorithm. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_96

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  • DOI: https://doi.org/10.1007/11539902_96

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28320-1

  • Online ISBN: 978-3-540-31863-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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