MicroGRID: An Accurate and Efficient Real-Time Stream Data Clustering with Noise-Reference-Cited by-同舟云学术

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MicroGRID: An Accurate and Efficient Real-Time Stream Data Clustering with Noise

Published:2018 Issue: Volume: Page:483-494
ISSN:0302-9743
Container-title:Advances in Knowledge Discovery and Data Mining
language:
Short-container-title:

Author:

Tari Z.,Thompson A.,Almusalam N.,Bertok P.,Mahmood A.

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-93037-4_38

Reference17 articles.

1. Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. In: Proceedings of the 29th International Conference on Very Large Data Bases, pp. 81–92 (2003)

2. Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for projected clustering of high dimensional data streams. In: Proceedings of the 13th International Conference on Very Large Data Bases, pp. 852–863 (2004)

3. Aggarwal, C.C., Yu, P.S.: A framework for clustering uncertain data streams. In: 24th Proceedings of the IEEE International Conference on Data Engineering, pp. 150–159 (2008)

4. Al Aghbari, Z., Kamel, I., Awad, T.: On clustering large number of data streams. Intell. Data Anal. 16(1), 69–91 (2012)

5. Amini, A., Wah, T.Y., Saybani, M.R., Yazdi, S.R.: A study of density-grid based clustering algorithms on data streams. In: Proceedings of the 8th IEEE International Conference on Fuzzy Systems and Knowledge Discovery, pp. 1652–1656 (2011)

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