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Improved Water Cycle Algorithm and K-Means Based Method for Data Clustering

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Intelligent Computing Methodologies (ICIC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12465))

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Abstract

K-means is a classical clustering method, but it is easy to fall into local optimums because of poor centers. Inspired by the good global search performance of Inter-Peer Communication Mechanism Based Water Cycle Algorithm (IPCWCA), three hybrid methods based on IPCWCA and K-means are presented in this paper, which are used to address the shortcoming of K-means and explore better clustering approaches. The hybrid methods consist of two modules successively: IPCWCA module and K-means module, which means that K-means module will inherit the best individual from IPCWCA module to start its clustering process. Compared with original K-means and WCA + K-means methods on eight datasets (including two customer segmentation datasets) based on SSE, accuracy and Friedman test, proposed methods show greater potential to solve clustering problems both in simple and customer segmentation datasets.

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References

  1. Pollard, D.: A central limit theorem for K-means clustering. Ann. Probab. 10(4), 919–926 (1982)

    Article  MathSciNet  Google Scholar 

  2. Dutta, D., Sil, J., Dutta, P.: Automatic clustering by multi-objective genetic algorithm with numeric and categorical features. Expert Syst. with Appl. 137, 357–379 (2019)

    Article  Google Scholar 

  3. Mustafi, D., Sahoo, G.: A hybrid approach using genetic algorithm and the differential evolution heuristic for enhanced initialization of the K-means algorithm with applications in text clustering. Soft. Comput. 23(15), 6361–6378 (2019)

    Article  Google Scholar 

  4. Gribel, D., Vidal, T.: HG-means: a scalable hybrid genetic algorithm for minimum sum-of-squares clustering. Pattern Recogn. 88, 569–583 (2019)

    Article  Google Scholar 

  5. Lai, D.T.C., Miyakawa, M., Sato, Y.: Semi-supervised data clustering using particle swarm optimisation. Soft. Comput. 24(5), 3499–3510 (2020)

    Article  Google Scholar 

  6. Janani, R., Vijayarani, S.: Text document clustering using spectral clustering algorithm with particle swarm optimization. Expert Syst. Appl. 134, 192–200 (2019)

    Article  Google Scholar 

  7. Liu, W.B., Wang, Z.D., Liu, X.H., Zeng, N.Y., Bell, D.: A novel particle swarm optimization approach for patient clustering from emergency departments. IEEE Trans. Evol. Comput. 23(4), 632–644 (2019)

    Article  Google Scholar 

  8. Menendez, H.D., Otero, F.E.B., Camacho, D.: Medoid-based clustering using ant colony optimization. Swarm Intell. 10(2), 123–145 (2016)

    Article  Google Scholar 

  9. Inkaya, T., Kayaligil, S., Ozdemirel, N.E.: Ant colony optimization based clustering methodology. Appl. Soft Comput. 28, 301–311 (2015)

    Article  Google Scholar 

  10. Eskandar, H., Sadollah, A., Bahreininejad, A., Hamdi, M.: Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput. Struct. 110–111(10), 151–166 (2012)

    Article  Google Scholar 

  11. Chen, C.H., Wang, P., Dong, H.C., Wang, X.J.: Hierarchical learning water cycle algorithm. Appl. Soft Comput. 86, p. 105935 (2020) https://doi.org/10.1016/j.asoc.2019

  12. Al-Rawashdeh, G., Mamat, R., Abd Rahim, N.H.B.: Hybrid water cycle optimization algorithm with simulated annealing for spam E-mail detection. IEEE Access. 7, 143721–143734 (2019)

    Article  Google Scholar 

  13. Bahreininejad, A.: Improving the performance of water cycle algorithm using augmented lagrangian method. Adv. Eng. Softw. 132, 55–64 (2019)

    Article  Google Scholar 

  14. Niu, B., Liu, H., Song, X.: An inter-peer communication mechanism based water cycle algorithm. In: Tan, Y., Shi, Y.H., Niu, B. (eds.) Advances in Swarm Intelligence. LNCS, vol. 11655, pp. 50–59. Springer, Chiang Mai (2019). https://doi.org/10.1007/978-3-030-26369-0_5

    Chapter  Google Scholar 

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Acknowledgement

The work described in this paper was supported by Innovating and Upgrading Institute Project from Department of Education of Guangdong Province (2017GWTSCX038), Innovative Talent Projects in Guangdong Universities (2018GWQNCX143), Guangdong Province Soft Science Project (2019A101002075), Guangdong Province Educational Science Plan 2019 (2019JKCY010) and Guangdong Province Postgraduate Education Innovation Research Project (2019SFKC46).

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Correspondence to Lijing Tan .

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Liu, H., Tan, L., Jin, L., Niu, B. (2020). Improved Water Cycle Algorithm and K-Means Based Method for Data Clustering. In: Huang, DS., Premaratne, P. (eds) Intelligent Computing Methodologies. ICIC 2020. Lecture Notes in Computer Science(), vol 12465. Springer, Cham. https://doi.org/10.1007/978-3-030-60796-8_50

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  • DOI: https://doi.org/10.1007/978-3-030-60796-8_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60795-1

  • Online ISBN: 978-3-030-60796-8

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