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On the Complexity of Some Problems of Searching for a Family of Disjoint Clusters

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Abstract

Two consimilar problems of searching for a family of disjoint subsets (clusters) in a finite set of points of Euclidean space are considered. In these problems, the task is to maximize the minimum cluster size so that the value of each intercluster quadratic variation does not exceed a given fraction (constant) of the total quadratic variation of the points of the input set with respect to its centroid. Both problems are proved to be NP-hard even on a line.

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REFERENCES

  1. D. Aloise, A. Deshpande, P. Hansen, and P. Popat, Machine Learning 75 (2), 245–248 (2009).

    Article  Google Scholar 

  2. P. Drineas, A. Frieze, R. Kannan, S. Vempala, and V. Vinay, Machine Learning 56, 9–33 (2004).

    Article  Google Scholar 

  3. S. Arora, P. Raghavan, and S. Rao, “Approximation schemes for Euclidean k-medians and related problems,” in Proceedings of the 30th Annual ACM Symposium on Theory of Computing (1998), pp. 106–113.

  4. S. Kariv and S. Hakimi, SIAM J. Appl. Math. 37, 513–538 (1979).

    Article  MathSciNet  Google Scholar 

  5. T. Feder and D. Greene, “Optimal algorithms for approximate clustering,” in Proceedings of the 20th ACM Symposium on Theory of Computing (New York, 1988), pp. 434–444.

  6. D. S. Hochbaum and D. B. Shmoys, Math. Operat. Res. 10 (2), 180–184 (1985).

    Article  Google Scholar 

  7. L. Kaufman and P. J. Rousseeuw, “Clustering by means of medoids,” in Statistical Data Analysis Based on the L 1 -Norm and Related Methods, Ed. by Y. Dodge (North-Holland, Amsterdam, 1987), pp. 405–416.

    Google Scholar 

  8. J. Krarup and P. Pruzan, Eur J. Oper. Res. 12 (1), 36–81 (1983).

    Article  Google Scholar 

  9. Discrete Location Theory, Ed. by P. Mirchandani and R. Francis (Wiley-Interscience, London, 1990).

    MATH  Google Scholar 

  10. M. Charikar, S. Khuller, D. M. Mount, and G. Narasimhan, “Algorithms for facility location problems with outliers,” in Proceedings of the 12th ACM-SIAM Symposium on Discrete Algorithms (Washington, 2001), pp. 642–651.

  11. P. K. Agarwal and J. M. Phillips, “An efficient algorithm for 2D Euclidean 2-center with outliers,” in Proceedings of the 16th Annual European Symposium on Algorithms (Karlsruhe, 2008), pp. 64–75.

  12. R. M. McCutchen and S. Khuller, “Streaming algorithms for \(k\)-center clustering with outliers and with anonymity,” in Proceedings of the 11th International Workshop on Approximation Algorithms (Springer, Berlin, 2008), pp. 165–178.

  13. B. Hatami and H. Zarrabi-Zade, Comput. Geom. 60, 26–36 (2017).

    Article  MathSciNet  Google Scholar 

  14. M. R. Garey and D. S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness (Freeman, San Francisco, 1979).

    MATH  Google Scholar 

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Correspondence to A. V. Kel’manov, A. V. Pyatkin or V. I. Khandeev.

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Translated by I. Ruzanova

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Kel’manov, A.V., Pyatkin, A.V. & Khandeev, V.I. On the Complexity of Some Problems of Searching for a Family of Disjoint Clusters. Dokl. Math. 99, 52–56 (2019). https://doi.org/10.1134/S1064562419010162

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  • DOI: https://doi.org/10.1134/S1064562419010162

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