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Differential Evolution Memetic Document Clustering Using Chaotic Logistic Local Search

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10634))

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Abstract

In this paper, we propose a Memetic-based clustering method that improves the partitioning of document clustering. Our proposed method is named as Differential Evolution Memetic Clustering (DEMC). Differential Evolution (DE) is used for the selection of the best set of cluster centres (centroids) while the Chaotic Logistic Search (CLS) is used to enhance the best set of solutions found by DE. For the purpose of comparison, the DEMC is compared with the basic DE, Differential Evolution Simulated Annealing (DESA) and the Differential Evolution K-Means (DEKM) methods as well as the traditional partitioning clustering using the K-means. The DEMC is also compared with the recently proposed Chaotic Gradient Artificial Bee Colony (CGABC) document clustering method. The reuters-21578, a pair of the 20-news group, classic 3 and TDT benchmark collection (TDT5) along with real-world six-event-crimes datasets are used in the experiments in this paper. The results showed that the proposed DEMC outperformed the other methods in terms of the convergence rate measured by the fitness function (ADDC) and the compactness of the resulted clusters measured by the F-macro and F-micro measures.

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Acknowledgements

Ibraheem would like to express his gratitude to the Higher Committee of Education Development in Iraq (HECD) for the scholarship he has received to fund his PhD study.

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Authors and Affiliations

  1. School of Engineering and Information Technology, Murdoch University, Perth, Australia

    Ibraheem Al-Jadir, Kok Wai Wong, Chun Che Fung & Hong Xie

  2. College of Science, Baghdad University, Baghdad, Iraq

    Ibraheem Al-Jadir

Authors
  1. Ibraheem Al-Jadir
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  2. Kok Wai Wong
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  3. Chun Che Fung
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  4. Hong Xie
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Corresponding author

Correspondence to Ibraheem Al-Jadir .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Al-Jadir, I., Wong, K.W., Fung, C.C., Xie, H. (2017). Differential Evolution Memetic Document Clustering Using Chaotic Logistic Local Search. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_23

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  • DOI: https://doi.org/10.1007/978-3-319-70087-8_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70086-1

  • Online ISBN: 978-3-319-70087-8

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