A new initialization and performance measure for the rough k-means clustering

Author:

Murugesan Vijaya Prabhagar,Murugesan Punniyamoorthy

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Reference38 articles.

1. Arthur D, Vassilvitskii S (2007) K-Means++: the advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on discrete algorithms, pp 1027–1025. https://doi.org/10.1145/1283383.1283494

2. Bhargava R, Tripathy BK, Tripathy A et al (2013) Rough intuitionistic fuzzy C-means algorithm and a comparative analysis. In: Compute 2013—6th ACM India computing convention: next generation computing paradigms and technologies

3. Bradley PS, Fayyad UM (1998) Refining initial points for K-means clustering. In: ICML proceedings of the fifteenth international conference on machine learning, 24–27 July 1998, pp 91–99. ISBN:1-55860-556-8

4. Bubeck S, Meila M, von Luxburg U (2009) How the initialization affects the stability of the k-means algorithm. ESAIM Probab Stat 16:436–452. https://doi.org/10.1051/ps/2012013

5. Darken C, Moody J (1990) Fast adaptive k-means clustering: some empirical results. In: IJCNN international joint conference on neural networks, San Diego, CA, USA, 17–21 June 1990, pp 233–238. https://doi.org/10.1109/IJCNN.1990.137720

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