Skip to main content

2-Approximation Polynomial-Time Algorithm for a Cardinality-Weighted 2-Partitioning Problem of a Sequence

  • Conference paper
  • First Online:
Numerical Computations: Theory and Algorithms (NUMTA 2019)

Abstract

We consider a problem of 2-partitioning a finite sequence of points in Euclidean space into clusters of the given sizes with some constraints. The solution criterion is the minimum of the sum of weighted intracluster sums of squared distances between the elements of each cluster and its center. The weight of the intracluster sum is equal to the cluster size. The center of one cluster is given as input (is the origin without loss of generality), while the center of the other one is unknown and is determined as a geometric center. The following constraints hold: the difference between the indices of two subsequent points included in the first cluster is bounded from above and below by some given constants. In this paper, we have shown that the considered problem is the strongly NP-hard one and propose a polynomial-time 2-approximation algorithm for solving the problem.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Fu, T.: A review on time series data mining. Eng. Appl. Artif. Intell. 24(1), 164–181 (2011)

    Article  Google Scholar 

  2. Kuenzer, C., Dech, S., Wagner, W.: Remote Sensing Time Series. RDIP, vol. 22. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15967-6

    Book  Google Scholar 

  3. Liao, T.W.: Clustering of time series data – a survey. Pattern Recogn. 38(11), 1857–1874 (2005)

    Article  Google Scholar 

  4. Kel’manov, A.V., Jeon, B.: A posteriori joint detection and discrimination of pulses in a quasiperiodic pulse train. IEEE Trans. Signal Process. 52(3), 645–656 (2004)

    Article  MathSciNet  Google Scholar 

  5. Carter, J.A., Agol, E., et al.: Kepler-36: a pair of planets with neighboring orbits and dissimilar densities. Science 337(6094), 556–559 (2012)

    Article  Google Scholar 

  6. Kel’manov, A.V., Pyatkin, A.V.: NP-hardness of some quadratic Euclidean 2-clustering problems. Dokl. Math. 92(2), 634–637 (2015)

    Article  MathSciNet  Google Scholar 

  7. Kel’manov, A.V., Motkova, A.V.: Exact Pseudopolynomial Algorithms for a Balanced 2-Clustering Problem. J. Appl. Ind. Math. 10(3), 349–355 (2016)

    Article  MathSciNet  Google Scholar 

  8. Kel’manov, A., Motkova, A.: A fully polynomial-time approximation scheme for a special case of a balanced 2-clustering problem. In: Kochetov, Y., Khachay, M., Beresnev, V., Nurminski, E., Pardalos, P. (eds.) DOOR 2016. LNCS, vol. 9869, pp. 182–192. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44914-2_15

    Chapter  Google Scholar 

  9. Kel’manov, A., Motkova, A., Shenmaier, V.: An approximation scheme for a weighted two-cluster partition problem. In: van der Aalst, W., et al. (eds.) AIST 2017. LNCS, vol. 10716, pp. 323–333. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73013-4_30

    Chapter  Google Scholar 

  10. Kel’manov, A.V., Motkova, A.V.: Polynomial-time approximation algorithm for the problem of cardinality-weighted variance-based 2-clustering with a given center. Comput. Math. Math. Phys. 58(1), 130–136 (2018)

    Article  MathSciNet  Google Scholar 

  11. Kel’manov, A., Khandeev, V., Panasenko, A.: Randomized algorithms for some clustering problems. In: Eremeev, A., Khachay, M., Kochetov, Y., Pardalos, P. (eds.) OPTA 2018. CCIS, vol. 871, pp. 109–119. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93800-4_9

    Chapter  Google Scholar 

  12. Kel’manov, A.V., Khamidullin, S.A.: Posterion detection of a given number of identical subsequences in a quasi-periodic sequence. Comp. Math. Math. Phys. 41(5), 762–774 (2001)

    MATH  Google Scholar 

  13. Kel’manov, A.V., Khamidullin, S.A.: An approximation polynomial algorithm for a sequence partitioning problem. J. Appl. Ind. Math. 8(2), 236–244 (2014)

    Article  MathSciNet  Google Scholar 

  14. Kel’manov, A.V., Khamidullin, S.A., Khandeev, V.I.: Exact pseudopolynomial algorithm for one sequence partitioning problem. Autom. Remote Control 78(1), 67–74 (2017)

    Article  MathSciNet  Google Scholar 

  15. Kel’manov, A.V., Romanchenko, S.M.: An FPTAS for a vector subset search problem. J. Appl. Ind. Math. 8(3), 329–336 (2014)

    Article  MathSciNet  Google Scholar 

  16. Kel’manov, A.V., Romanchenko, S.M.: An approximation algorithm for solving a problem of search for a vector subset. J. Appl. Ind. Math. 6(1), 90–96 (2012)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

The study presented in Sects. 3 and 4 was supported by the Russian Foundation for Basic Research, projects 19-07-00397, 19-01-00308 and 18-31-00398. The study presented in the other sections was supported by the Russian Academy of Science (the Program of basic research), project 0314-2019-0015, and by the Russian Ministry of Science and Education under the 5-100 Excellence Programme.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anna Panasenko .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kel’manov, A., Khamidullin, S., Panasenko, A. (2020). 2-Approximation Polynomial-Time Algorithm for a Cardinality-Weighted 2-Partitioning Problem of a Sequence. In: Sergeyev, Y., Kvasov, D. (eds) Numerical Computations: Theory and Algorithms. NUMTA 2019. Lecture Notes in Computer Science(), vol 11974. Springer, Cham. https://doi.org/10.1007/978-3-030-40616-5_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-40616-5_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-40615-8

  • Online ISBN: 978-3-030-40616-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics