Ensemble Learning Based Distributed Clustering

Author:

Ji Genlin,Ling Xiaohan

Publisher

Springer Berlin Heidelberg

Reference13 articles.

1. MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: LeCam, L.M., Neyman, J. (eds.) Proc. of the 5th Berkeley Symp. on Mathematical Statistics and Probability, Berkeley, vol. 1, pp. 281–297. University of California Press (1967)

2. McLachlan, G., Basford, K.: Mixture Models: Inference and Application to Clustering, Marcel Dekker, New York (1988)

3. Ester, M., Kriegel, H.P., Sander, J., et al.: A density based algorithm of discovering clusters in large spatial databases with noise. In: Simoudis, E., Jiawei, H., Fayyad, U.M. (eds.) Proc. of the 2nd Int. Conf. on Knowledge Discovery and Data Mining, Portland, Oregon, pp. 226–231. AAAI Press, Stanford, California, USA (1996)

4. Park, B.H., Kargupta, H.: Distributed Data Mining: Algorithms, Systems, and Applications. In: Ye, N. (ed.) The Handbook of Data Mining, Lawrence Erlbaum Associates Publishers, Mahwah, NJ (2003)

5. Lecture Notes in Artificial Intelligence;E. Januzaj,2004

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