Density-based hierarchical clustering for streaming data
Author:
Publisher
Elsevier BV
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software
Reference18 articles.
1. Aggarwal, C., Han, J.W., Wang, J., 2003. A framework for clustering evolving data streams. In: Proc. 29th Internat. Conf. on Very large databases, pp. 81–92.
2. Ankerst, M., Breunig, M.M., Kriegel, H.P., 1999. OPTICS: Ordering points to identify the clustering structure. In: ACM SIGMOD’99 Internat. Conf. on Management of Data, Philadelphia, pp. 40–60.
3. Babcock, B., Babu, S., Datar, M., 2002. Models and issues in data streams. In: Proc. ACM SIGACT-SIGMOD Symp. on Principles of Database Systems, pp. 1–16.
4. Cao, F., Ester, M., Qian, W., 2006. Density-based clustering over an evolving data stream with noise. In: Proc. SIAM Conf. on Data Ming, pp. 328–339.
5. Chen, Y.X., Tu, L., 2007. Density-based clustering for real-time stream data. IN: Internat. Conf. on Knowledge Discovery and Data Mining, pp. 133–142.
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