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Parallelizing the Unsupervised k-Windows Clustering Algorithm

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Parallel Processing and Applied Mathematics (PPAM 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3019))

Abstract

Clustering can be defined as the process of partitioning a set of patterns into disjoint and homogeneous meaningful groups, called clusters. The growing need for parallel clustering algorithms is attributed to the huge size of databases that is common nowadays. This paper presents a parallel version of a recently proposed algorithm that has the ability to scale very well in parallel environments mainly regarding space requirements but also gaining a time speedup.

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References

  1. Aldenderfer, M.S., Blashfield, R.K.: Cluster Analysis. Quantitative Applications in the Social Sciences. SAGE Publications, London (1984)

    Google Scholar 

  2. Alevizos, P.: An Algorithm for Orthogonal Range Search in d \(\geqslant\) 3 Dimensions. In: Proceedings of the 14th European Workshop on Computational Geometry, Barcelona (1998)

    Google Scholar 

  3. Alevizos, P., Boutsinas, B., Tasoulis, D., Vrahatis, M.N.: Improving the Orthogonal Range Search k-windows Clustering Algorithm. In: Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence, Washington D.C., pp. 239–245 (2002)

    Google Scholar 

  4. Bentley, J.L., Maurer, H.A.: Efficient Worst-Case Data Structures for Range Searching. Acta Informatica 13, 1551–1568 (1980)

    Article  MathSciNet  Google Scholar 

  5. Chazelle, B.: Filtering Search: A New Approach to Query-Answering. SIAM J. Comput. 15(3), 703–724 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  6. Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: Advances in Knowledge Discovery and Data Mining. MIT Press, Cambridge (1996)

    Google Scholar 

  7. Geist, A., Beguelin, J., Dongarra, W., Jiang, R., Manchek, V.: PVM: Parallel Virtual Machine. In: A User’s Guide and Tutorial for Networked Parallel Computing. MIT Press, Cambridge (1994)

    Google Scholar 

  8. Chazelle, B., Guibas, L.J.: Fractional Cascading: II. Applications. Algorithmica 1, 163–191 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  9. Judd, D., McKinley, P., Jain, A.: Large-Scale Parallel Data Clustering. In: Proceedings of the Int. Conference on Pattern Recognition (1996)

    Google Scholar 

  10. Judd, D., McKinley, P., Jain, A.: Performance Evaluation on Large-Scale Parallel Clustering in NOW Environments. In: Proceedings of the Eight SIAM Conference on Parallel Processing for Scientific Computing, Minneapolis (March 1997)

    Google Scholar 

  11. MPI The Message Passing Interface standard, http://www-unix.mcs.anl.gov/mpi/

  12. Olson, C.F.: Parallel Algorithms for Hierarchical Clustering. Parallel Computing 21, 1313–1325 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  13. Preparata, F., Shamos, M.: Computational Geometry. Springer, Heidelberg (1985)

    Google Scholar 

  14. Potts, J.T.: Seeking Parallelism in Discovery Programs, Master Thesis, University of Texas at Arlington (1996)

    Google Scholar 

  15. Ramasubramanian, V., Paliwal, K.: Fast k-dimensional Tree Algorithms for Nearest Neighbor Search with Application to Vector Quantization Encoding. IEEE Transactions on Signal Processing 40(3), 518–531 (1992)

    Article  Google Scholar 

  16. Stoffel, K., Belkoniene, A.: Parallel k-means Clustering for Large Data Sets. In: Amestoy, P.R., Berger, P., Daydé, M., Duff, I.S., Frayssé, V., Giraud, L., Ruiz, D. (eds.) Euro-Par 1999. LNCS, vol. 1685, pp. 1451–1454. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  17. Vrahatis, M.N., Boutsinas, B., Alevizos, P., Pavlides, G.: The New k-windows Algorithm for Improving the k-means Clustering Algorithm. Journal of Complexity 18, 375–391 (2002)

    Article  MATH  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mathematics, University of Patras, GR-26500, Patras, Greece

    Panagiotis D. Alevizos, Dimitris K. Tasoulis & Michael N. Vrahatis

  2. University of Patras Artificial Intelligence Research Center (UPAIRC), University of Patras, GR-26500, Patras, Greece

    Panagiotis D. Alevizos, Dimitris K. Tasoulis & Michael N. Vrahatis

Authors
  1. Panagiotis D. Alevizos
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  2. Dimitris K. Tasoulis
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  3. Michael N. Vrahatis
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Editor information

Editors and Affiliations

  1. Institute of Computational and Information Sciences, Czestochowa University of Technology,  

    Roman Wyrzykowski

  2. Computer Science Department, University of Tennessee, TN 37996-3450, Knoxville, USA

    Jack Dongarra

  3. Systems Research Institute, Polish Academy of Science, Warsaw, Poland

    Marcin Paprzycki

  4. Informatics & Mathematical Modeling, Technical University of Denmark, DK-2800, Lyngby, Denmark

    Jerzy Waśniewski

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© 2004 Springer-Verlag Berlin Heidelberg

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Alevizos, P.D., Tasoulis, D.K., Vrahatis, M.N. (2004). Parallelizing the Unsupervised k-Windows Clustering Algorithm. In: Wyrzykowski, R., Dongarra, J., Paprzycki, M., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2003. Lecture Notes in Computer Science, vol 3019. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24669-5_29

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  • DOI: https://doi.org/10.1007/978-3-540-24669-5_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21946-0

  • Online ISBN: 978-3-540-24669-5

  • eBook Packages: Springer Book Archive

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