A varied density-based clustering algorithm

Author:

Fahim Ahmed

Publisher

Elsevier BV

Subject

Modeling and Simulation,General Computer Science,Theoretical Computer Science

Reference31 articles.

1. M. Ester, H.-P. Kriegel, J. Sander, X. Xiaowei, A Density-based algorithm for discovering clusters in large spatial databases with noise, in Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), 1996, pp. 226–231.

2. OPTICS: ordering points to identify the clustering structure;Ankerst;ACM SIGMOD Rec.,1999

3. An efficient approach to clustering in large multimedia databases with noise;Hinneburg;Proc. Fourth Int. Conf. Knowl. Discov. Data Min.,1998

4. Efficient enhanced k-means clustering algorithm;Fahim;J. Zhejiang Univ. Sci.,2006

5. J.E. Gentle, L. Kaufman, and P.J. Rousseuw, Finding Groups in Data: An Introduction to Cluster Analysis., vol. 47, no. 2. 1991.

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