An Approach for Clustering Text Data Streams Using K-means and Ternary Feature Vector Based Similarity Measure-Reference-Cited by-同舟云学术

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An Approach for Clustering Text Data Streams Using K-means and Ternary Feature Vector Based Similarity Measure

Published:2015 Issue: Volume: Page:
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Container-title:Proceedings of the The International Conference on Engineering & MIS 2015 - ICEMIS '15
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Author:

PhridviRaj M. S. B.,Rao C. V. Guru

Publisher

ACM Press

Reference18 articles.

1. Jonathan A. Silva, Elaine R. Faria, Rodrigo C. Barros, Eduardo R. Hruschka, André C. P. L. F. de Carvalho, and Joao Gama. 2013. Data stream clustering: A survey. ACM Comput. Surv. 46, 1, Article 13 (July 2013), 31 pages.

2. Zaher Al Aghbari, Ibrahim Kamel, and Thuraya Awad. 2012. On clustering large number of data streams. Intell. Data Anal. 16, 1 (January 2012), 69--91. DOI=10.3233/IDA-2011-0511

3. Sebastian Lühr and Mihai Lazarescu. 2009. Incremental clustering of dynamic data streams using connectivity based representative points. Data Knowl. Eng. 68, 1 (January 2009), 1--27.

4. Shifei Ding, Fulin Wu, Jun Qian, Hongjie Jia, and Fengxiang Jin. 2015. Research on data stream clustering algorithms. Artif. Intell. Rev. 43, 4 (April 2015), 593--600. DOI=10.1007/s10462-013-9398-7

5. Marcelo Keese Albertini and Rodrigo Fernandes Mello. 2013. Energy-based function to evaluate data stream clustering. Adv. Data Anal. Classif. 7, 4 (December 2013), 435--464.

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