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A Hybrid Data Clustering Approach Based on Hydrologic Cycle Optimization and K-means

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Bio-inspired Computing: Theories and Applications (BIC-TA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 952))

Abstract

K-means is a popular and simple clustering method by grouping data into predefined K clusters efficiently. However, K-means performs poorly in the presence of poor centers and tends to converge prematurely. Hydrologic Cycle Optimization, as a novel algorithm inspired by the natural phenomena, has a good ability to search for the global optimal solutions. To overcome drawbacks associated with the K-means and find better initial centroids, in this study, a hybrid clustering algorithm based on Hydrologic Cycle Optimization and K-means (abbreviated as HCO+K-means) is proposed. The proposed algorithm includes two modules: HCO module and K-means module. It executes HCO module firstly to find the best individual with optimal fitness value. While the position of the best individual is then considered as initial set of centers for K-means module to search for a higher quality clustering solution. For comparison purpose, the K-means, PSO+K-means, WCA+K-means and HCO+K-means algorithms are chosen to evaluate on six different datasets. The experimental results indicate that the proposed HCO+K-means algorithm has a strong global search ability and obtains better clustering results in comparison to the other clustering methods.

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Acknowledgment

This work is partially supported by The National Natural Science Foundation of China (Grants Nos. 71571120, 71271140, 71771154, 61603310, 71471158, 71001072, 61472257), and Research Foundation of Shenzhen University (85303/00000155).

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Authors and Affiliations

  1. College of Management, Shenzhen University, Shenzhen, 518060, China

    Ben Niu, Huan Liu, Lei Liu & Hong Wang

Authors
  1. Ben Niu
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  2. Huan Liu
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  3. Lei Liu
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  4. Hong Wang
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Corresponding author

Correspondence to Hong Wang .

Editor information

Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Jianyong Qiao

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Xinchao Zhao

  3. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  4. Beijing University of Posts and Telecommunications, Beijing, China

    Xingquan Zuo

  5. Anhui University, Hefei, China

    Xingyi Zhang

  6. City University of Hong Kong, Kowloon, Hong Kong

    Qingfu Zhang

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Shanguo Huang

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Niu, B., Liu, H., Liu, L., Wang, H. (2018). A Hybrid Data Clustering Approach Based on Hydrologic Cycle Optimization and K-means. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 952. Springer, Singapore. https://doi.org/10.1007/978-981-13-2829-9_30

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  • DOI: https://doi.org/10.1007/978-981-13-2829-9_30

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2828-2

  • Online ISBN: 978-981-13-2829-9

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