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Elephant search algorithm applied to data clustering

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Abstract

Data clustering is one of the most popular branches of machine learning and data analysis. Partitioning-based type of clustering algorithms, such as K-means, is prone to the problem of producing a set of clusters that is far from perfect due to its probabilistic nature. The clustering process starts with some random partitions at the beginning, and then it attempts to improve the partitions progressively. Different initial partitions can result in different final clusters. Trying through all the possible candidate clusters for the perfect result is computationally expensive. Meta-heuristic algorithm aims to search for global optimum in high-dimensional problems. Meta-heuristic algorithm has been successfully implemented on data clustering problems seeking a near optimal solution in terms of quality of the resultant clusters. In this paper, a new meta-heuristic search method named elephant search algorithm (ESA) is proposed to integrate into K-means, forming a new data clustering algorithm, namely C-ESA. The advantage of C-ESA is its dual features of (i) evolutionary operations and (ii) balance of local intensification and global exploration. The results by C-ESA are compared with classical clustering algorithms including K-means, DBSCAN, and GMM-EM. C-ESA is shown to outperform the other algorithms in terms of clustering accuracy via a computer simulation. C-ESA is also implemented on time series clustering compared with classical algorithms K-means, Fuzzy C-means and classical meta-heuristic algorithm PSO. C-ESA outperforms the other algorithms in term of clustering accuracy. C-ESA is still comparable compared with state of art time series clustering algorithm K-shape.

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Acknowledgements

The authors are grateful for financial support from the research Grants (1) ‘Nature-Inspired Computing and Metaheuristics Algorithms for Optimizing Data Mining Performance’ from the University of Macau (Grant No. MYRG2016-00069-FST); (2) ‘Temporal Data Stream Mining by Using Incrementally Optimized Very Fast Decision Forest (iOVFDF)’, which are offered by the University of Macau, (Grant No. MYRG2015-00128-FST); and (3) ‘A Scalable Data Stream Mining Methodology: Stream-based Holistic Analytics and Reasoning in Parallel’, from FDCT, Macau SAR government (Grant No. FDCT/126/2014/A3).

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Authors and Affiliations

  1. IT and Educational Consultant, Ranchi, Jharkhand, India

    Suash Deb

  2. Department of Computer and Information Science, University of Macau, Taipa, Macau SAR, China

    Zhonghuan Tian & Simon Fong

  3. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia

    Raymond Wong

  4. Department of Information Technology, Durban University of Technology, Durban, South Africa

    Richard Millham

  5. Centre for Biomedical Engineering, Department of Electronic and Electrical Engineering, University of Adelaide, Adelaide, Australia

    Kelvin K. L. Wong

  6. School of Medicine, University of Western Sydney, Campbelltown, Sydney, Australia

    Kelvin K. L. Wong

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  1. Suash Deb
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  2. Zhonghuan Tian
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  3. Simon Fong
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  4. Raymond Wong
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  5. Richard Millham
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  6. Kelvin K. L. Wong
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Correspondence to Kelvin K. L. Wong.

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The authors declare that they have no conflict of interest.

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Communicated by S. Deb, T. Hanne, K.C. Wong.

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Deb, S., Tian, Z., Fong, S. et al. Elephant search algorithm applied to data clustering. Soft Comput 22, 6035–6046 (2018). https://doi.org/10.1007/s00500-018-3076-2

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  • DOI: https://doi.org/10.1007/s00500-018-3076-2

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