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International Conference on Artificial Intelligence and Soft Computing

ICAISC 2017: Artificial Intelligence and Soft Computing pp 94–103Cite as

Two Modifications of Yinyang K-means Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10246))

Abstract

In the paper a very fast algorithm for K-means clustering problem, called Yinyang K-means, is considered. The algorithm uses initial grouping of cluster centroids and the triangle inequality to avoid unnecessary distance calculations. We propose two modifications of Yinyang K-means: regrouping of cluster centroids during the run of the algorithm and replacement of the grouping procedure with a method, which generates the groups of equal sizes. The influence of these two modifications on the efficiency of Yinyang K-means is experimentally evaluated using seven datasets. The results indicate that new grouping procedure reduces runtime of the algorithm. For one of tested datasets it runs up to 2.8 times faster.

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References

  1. Aloise, D., Deshpande, A., Hansen, P., Popat, P.: NP-hardness of euclidean sum-of-squares clustering. Mach. Learn. 75(2), 245–248 (2009)

    Article  Google Scholar 

  2. Arthur, D., Vassilvitskii, S.: K-means++: The advantages of careful seeding. In: Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2007), pp. 1027–1035 (2007)

    Google Scholar 

  3. Bottesch, T., Bühler, T., Käechele, M.: Speeding up k-means by approximating Euclidean distances via block vectors. In: Proceedings of The 33rd International Conference on Machine Learning, pp. 2578–2586 (2016)

    Google Scholar 

  4. Dagum, L., Menon, R.: OpenMP: an industry standard API for shared-memory programming. IEEE Comput. Sci. Eng. 5(1), 46–55 (1998)

    Article  Google Scholar 

  5. Ding, Y., Zhao, Y., Shen, X., Musuvathi, M., Mytkowicz, T.: Yinyang k-means: a drop-in replacement of the classic k-means with consistent speedup. In: Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), pp. 579–587 (2015)

    Google Scholar 

  6. Elkan, C.: Using the triangle inequality to accelerate k-means. In: Proceedings of the 20th International Conference on Machine Learning (ICML 2003), vol. 3, pp. 147–153. AAAI Press (2003)

    Google Scholar 

  7. Hamerly, G., Drake, J.: Accelerating Lloyds algorithm for k-means clustering. In: Celebi, M.E. (ed.) Partitional Clustering Algorithms, pp. 41–78. Springer, Heidelberg (2015)

    Google Scholar 

  8. Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)

    Article  Google Scholar 

  9. Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)

    Article  MATH  Google Scholar 

  10. Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml

  11. Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–137 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  12. Maitra, R., Melnykov, V.: Simulating data to study performance of finite mixture modeling and clustering algorithms. J. Comput. Graph. Stat. 19(2), 354–376 (2010)

    Article  MathSciNet  Google Scholar 

  13. Pelleg, D., Moore, A.: Accelerating exact k-means algorithms with geometric reasoning. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 277–281 (1999)

    Google Scholar 

  14. Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2007)

    Google Scholar 

  15. Wang, J., Wang, J., Ke, Q., Zeng, G., Li, S.: Fast approximate k-means via cluster closures. In: Baughman, A.K., Gao, J., Pan, J.Y., Petrushin, V.A. (eds.) Multimedia Data Mining and Analytics, pp. 373–395. Springer, Heidelberg (2015)

    Google Scholar 

  16. Wang, J., Wang, N., Jia, Y., Li, J., Zeng, G., Zha, H., Hua, X.S.: Trinary-projection trees for approximate nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell. 36(2), 388–403 (2014)

    Article  Google Scholar 

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Acknowledgments

This work was supported by the Bialystok University of Technology grant S/WI/2/2013 funded by the Polish Ministry of Science and Higher Education. This research was carried out with the support of the Interdisciplinary Centre for Mathematical and Computational Modelling (ICM) University of Warsaw under grant no G65-12. The author is grateful to Dr. Jingdong Wang for providing access to caltech101, notredame, tiny and ukbench datasets.

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Authors and Affiliations

  1. Faculty of Computer Science, Bialystok University of Technology, Wiejska 45a, 15-351, Bialystok, Poland

    Wojciech Kwedlo

Authors
  1. Wojciech Kwedlo
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Correspondence to Wojciech Kwedlo .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  4. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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Kwedlo, W. (2017). Two Modifications of Yinyang K-means Algorithm. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10246. Springer, Cham. https://doi.org/10.1007/978-3-319-59060-8_10

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  • DOI: https://doi.org/10.1007/978-3-319-59060-8_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59059-2

  • Online ISBN: 978-3-319-59060-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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