Dynamic load balancing in parallel KD-tree k-means

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[1] J. B. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability, L. M. L. Cam and J. Neyman, Eds., vol. 1. University of California Press, 1967, pp. 281–297. [2] S. P. Lloyd, “Least squares quantization in PCM,” IEEE Transactions on Information Theory, vol. IT-28, no. 2, pp. 129–137, Mar. 1982. [3] A. W. Moore, “Efficient memory based learning for robot control,” PhD thesis, 1991. [4] K. Alsabti, S. Ranka, and V. Singh, “An efficient K-Means clustering algorithm,” Proceedings of First Workshop on High Performance Data Mining, 1998. [5] D. Pelleg and A. Moore, “Accelerating exact K-Means algorithms with geometric reasoning,” Proceedings of Fifth ACM SIGKDD Intl. Conference on Knowledge Discovery and Data Mining, pp. 277–281, Jul. 1999. [6] T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, “An efficient K-Means clustering algorithm: Analysis and implementation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 881–892, Jul. 2002. [7] I. S. Dhillon and D. S. Modha, “A data-clustering algorithm on distributed memory multiprocessors,” In Large-Scale Parallel Data Mining, Lecture Notes in Computer Science, vol. 1759, pp. 245–260, Mar. 2000. [8] A. G¨ursoy, “Data decomposition for K-Means parallel clustering,” in Proceedings of the 5th International Conference on Parallel Processing and Applied Mathematics (PPAM), 2003, pp. 241–248. [9] M. Inaba, N. Katoh, and H. Imai, “Applications of weighted Voronoi diagrams and randomization to variance-based kclustering,” in SCG ’94: Proceedings of the tenth annual symposium on Computational geometry. NewYork,NY, USA: ACM, 1994, pp. 332–339. [10] D. Judd, P. K. McKinley, and A. K. Jain, “Large-scale parallel data clustering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 871–876, Aug. 1998. [11] S. Har-Peled and B. Sadri, “How fast is the k-means method?” in SODA ’05: Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms. Philadelphia, PA, USA: Society for Industrial and Applied Mathematics, 2005, pp. 877–885. [12] D. Arthur and S. Vassilvitskii, “How slow is the k-means method?” in SCG ’06: Proceedings of the twenty-second annual symposium on Computational geometry. NewYork, NY, USA: ACM, 2006, pp. 144–153. [13] P. S. Bradley and U. M. Fayyad, “Refining initial points for KMeans clustering,” Proceedings of Fifteenth Intl. Conference on Machine Learning, pp. 91–99, 1998. [14] D. Pelleg and A. Moore, “X-Means: Extending K-Means with efficient estimation of the number of clusters,” Proceedings of the Seventeenth Intl. Conference on Machine Learning, pp. 727–734, 2000. [15] J. L. Bentley, “Multidimensional binary search trees used for associative searching,” Communications of the ACM, vol. 18, no. 9, pp. 509–517, 1975. [16] T. Zhang, R. Ramakrishnan, and M. Livny, “Birch: an efficient data clustering method for very large databases,” SIGMOD Rec., vol. 25, no. 2, pp. 103–114, 1996. [17] D. Foti, D. Lipari, C. Pizzuti, and D. Talia, “Scalable parallel clustering for data mining on multicomputers,” Proceedings of the 15th IPDPS 2000 Workshops on Parallel and Distributed Processing, pp. 390–398, 2000. [18] I. Al-furaih, S. Aluru, S. Goil, and S. Ranka, “Parallel construction of multidimensional binary search trees,” IEEE Transactions on Parallel and Distributed Systems, vol. 11, no. 2, pp. 136–148, Feb. 2000. [19] J.B. Kruskal and M. Wish, Multidimensional Scaling. Sage University Paper series on Quantitative Applications in the Social Sciences, Beverly Hills and London: Sage Publications, 07-011, 1978. [20] M. Baker, B. Carpenter, and A. Shafi, “MPJ Express: Towards thread safe Java HPC,” Proceedings of the IEEE International Conference on Cluster Computing, Sep. 2006.