Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Reference :

    1. K. Alsabti, S. Ranka, and V. Singh. An Efficient KMeans Clustering Algorithm. ttp:// www. cise. ufl. edu / ranka/, 1997
    2. T. H. Cormen, C. E. Leiserson, and R. L. Rivest. Introductionto Algorithms. McGraw-Hill Book Company, 1990.
    3. R. C. Dubes and A. K. Jain. Algorithms for Clustering Data. Prentice Hall, 1988.
    4. M. Ester, H. Kriegel, J. Sander, and X. Xu. A DensityBased Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proc. of the 2nd Int’l Conf. on Knowledge Discovery and Data Mining, August 1996.
    5. M. Ester, H. Kriegel, and X. Xu. Knowledge Discovery in Large Spatial Databases: Focusing Techniques for Efficient Class Identification. Proc. of the Fourth Int’l. Symposium on Large Spatial Databases, 1995.
    6. J. Garcia, J. Fdez-Valdivia, F. Cortijo, and R. Molina. Dynamic Approach for Clustering Data. Signal Processing, 44:(2), 1994.
    7. D. Judd, P. McKinley, and A. Jain. Large-Scale Parallel Data Clustering. Proc. Int’l Conference on Pattern Recognition, August 1996
    8. L. Kaufman and P. J. Rousseeuw. Finding Groups in Data:an Introduction to Cluster Analysis. John Wiley & Sons, 1990.
    9. K. Mehrotra, C. Mohan, and S. Ranka. Elements of Artificial Neural Networks. MIT Press, 1996.
    10. R. T. Ng and J. Han. Efficient and Effective Clustering Methods for Spatial Data Mining. Proc. of the 20th Int’l Conf. on Very Large Databases, Santiago, Chile, pages 144– 155, 1994.
    11. V. Ramasubramanian and K. Paliwal. Fast KDimensional Tree Algorithms for Nearest Neighbor Search with Application to Vector Quantization Encoding. IEEE Transactions on Signal Processing, 40:(3), March 1992
    12. E. Schikuta. Grid Clustering: An Efficient Hierarchical Clustering Method for Very Large Data Sets. Proc. 13th Int’l. Conference on Pattern Recognition, 2, 1996.
    13. J. White, V. Faber, and J. Saltzman. United States Patent No. 5,467,110. Nov. 1995
    14. T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH: An Efficient Data Clustering Method for Very Large Databases. Proc. of the 1996 ACM SIGMOD Int’l Conf. on Management of Data, Montreal, Canada, pages 103–114, June 1996

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