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Autor
Poteraş Cosmin Marian (University of Craiova, Romania), Mihăescu Cristian (University of Craiova, Romania), Mocanu Mihai (University of Craiova, Romania)
Tytuł
An Optimized Version of the K-Means Clustering Algorithm
Źródło
Annals of Computer Science and Information Systems, 2014, vol. 2, s. 695 - 699, rys., tab., bibliogr. 15 poz.
Słowa kluczowe
Algorytmy, Optymalizacja, Czas pracy
Algorithms, Optimalization, Working time
Uwagi
summ.
Abstrakt
This paper introduces an optimized version of the standard K-Means algorithm. The optimization refers to the running time and it comes from the observation that after a certain number of iterations, only a small part of the data elements change their cluster, so there is no need to re-distribute all data elements. Therefore the implementation proposed in this paper puts an edge between those data elements which won't change their cluster during the next iteration and those who might change it, reducing significantly the workload in case of very big data sets. The prototype implementation showed up to 70% reduction of the running time.(original abstract)
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Bibliografia
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  1. Burdescu D.D., Mihaescu M.C., "Enhancing the Assessment Environment within a Learning Management Systems," EUROCON, 2007. The International Conference on "Computer as a Tool", vol., no., pp.2438,2443, 9-12 Sept. 2007
  2. Chen C. W., Luo J., Parker K. J. - Image Segmentation via Adaptive K-Mean Clustering and Knowledge-Based Morphological Operations with Biomedical Applications, IEEE Transactions on Image Processing, VOL. 7, NO. 12, DECEMBER 1998, pages 1673 - 1683
  3. Datta S., Giannella C., Kargupta H. - K-Means Clustering Over a Large, Dynamic Network, Proceedings of the Sixth SIAM International Conference on Data Mining, April 20-22, 2006, Bethesda, MD, USA. SIAM 2006 ISBN 978-0-89871-611-5, pages 153 - 164
  4. Dolnicar S, Using cluster analysis for market segmentation - typical misconceptions, established methodological weaknesses and some recommendations for improvement, Australasian Journal of Market Research, 2003, 11(2), 5-12.
  5. Fahim A.M., Salem A.M., Torkey F.A., Ramadan M.A. - An Efficient Enhanced K-means Clustering Algorithm Journal of Zhejiang University SCIENCE A, ISSN 1009-3095 (Print); ISSN 1862-1775 (Online), pages 1626 - 1633, 2006 7(10)
  6. Farivar R., Rebolledo D., Chan E., Campbell R. - A Parallel Implementation of K-Means Clustering on GPUs, Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, PDPTA 2008, Las Vegas, Nevada, USA, July 14-17, 2008, 2 Volumes. CSREA Press 2008 ISBN 1-60132-084-1, pages 340-345
  7. Kanungo T., Mount D. M., Piatko K. D., Netanyahu N. S., Silverman R., Wu A. Y. - An efficient k-means clustering algorithm: analysis and implementation, Pattern Analysis and Machine Intelligence, IEEE Transactions on (Volume:24, Issue: 7 ), pages 881-892, July 2002, ISSN 0162-8828
  8. Kumar J., Mills R. T., Hoffman F. M., Hargrove W. W. - Parallel k-Means Clustering for Quantitative Ecoregion Delineation Using Large Data Sets, Proceedings of the International Conference on Computational Science, ICCS 2011, Procedia Computer Science 4 (2011) 1602-1611
  9. Ng H.P., Ong S.H.; Foong, K.W.C.; Goh, P.S.; Nowinsky, W.L. - Medical Image Segmentation Using K-Means Clustering and Improved Watershed Algorithm, 7th IEEE Southwest Symposium on Image Analysis and Interpretation, March 26-28, 2006, Denver, Colorado, pages 61-66
  10. Othman F., Abdullah R., Rashid N. A., and Salam R. A. - Parallel K-Means Clustering Algorithm on DNA Dataset, Parallel and Distributed Computing: Applications and Technologies, Lecture Notes in Computer Science Volume 3320, 2005, pp 248-251
  11. Oyelade O. J., Oladipupo O. O, Obagbuwa I. C - Application of K-Means Clustering algorithm for prediction of Students' Academic Performance, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 7, No. 1, 2010, pages 292-295
  12. Xie H., Zhang L., Sun J., Yu X. - Application of Kmeans Clustering Algorithms in News Comments - The International Conference on E-Business and E-Government, May 2010, Guangzhou, China, pages 451-454
  13. Zechner M., Granitzer M. - Accelerating K-Means on the Graphics Processor via CUDA, The First International Conference on Intensive Applications and Services INTENSIVE 2009, 20-25 April, Valencia, Spain, pages 7-15, ISBN 978-1-4244-3683-5
  14. Zhang J., Wu G., Hu X., Li S., Hao S. - A Parallel K-means Clustering Algorithm with MPI, 4th Internation Symposium on Parallel Architectures, Algorithms and Programming, ISBN 978-0-7695-4575-2, pages 60-64, 2011
  15. Zhang Y., Xiong Z., Mao J., Ou L. - The Study of Parallel K-Means Algorithm, Proceedings of the 6th World Congress on Intelligent Control and Automation, June 21 - 23, 2006, Dalian, China, pages 5868 - 5871
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ISSN
2300-5963
Język
eng
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