An Improved Visual Assessment with Data-Dependent Kernel for Stream Clustering

Author:

Zhang Baojie,Cao Yang,Zhu Ye,Rajasegarar Sutharshan,Liu Gang,Li Hong Xian,Angelova Maia,Li Gang

Publisher

Springer Nature Switzerland

Reference27 articles.

1. Ackermann, M.R., Märtens, M., Raupach, C., Swierkot, K., Lammersen, C., Sohler, C.: Streamkm++ a clustering algorithm for data streams. J. Exp. Algorithmics (JEA) 17, 2–1 (2012)

2. Aggarwal, C.C., Philip, S.Y., Han, J., Wang, J.: A framework for clustering evolving data streams. In: Proceedings 2003 VLDB Conference, pp. 81–92. Elsevier (2003)

3. Arthur, D., Vassilvitskii, S.: K-means++: The advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2007, pp. 1027–1035. Society for Industrial and Applied Mathematics, USA (2007)

4. Bezdek, J. C. Hathaway, R.J.: Vat: a tool for visual assessment of (cluster) tendency. In: International Joint Conference on Neural Networks (2002)

5. Borgwardt, K.M., Gretton, A., Rasch, M.J., Kriegel, H.P., Schölkopf, B., Smola, A.J.: Integrating structured biological data by Kernel Maximum Mean Discrepancy. Bioinformatics 22(14), 49–57 (2006)

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