Data Stream Clustering in Conditions of an Unknown Amount of Classes-Reference-Cited by-同舟云学术

Data Stream Clustering in Conditions of an Unknown Amount of Classes

Published:2018-05-12 Issue: Volume: Page:410-418
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
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Author:

Zhernova Polina,Deyneko Anastasiya,Deyneko Zhanna,Pliss Irina,Ahafonov Volodymyr

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-91008-6_41

Reference26 articles.

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4. Zhengbing, H., Bodyanskiy, Y.V., Tyshchenko, O.K., Samitova, V.O.: Possibilistic fuzzy clustering for categorical data arrays based on frequency prototypes and dissimilarity measures. Int. J. Intell. Syst. Appl. (IJISA) 9(5), 55–61 (2017). https://doi.org/10.5815/ijisa.2017.05.07

5. Pelleg, D., Moor, A.: X-means: extending K-means with efficient estimation of the number of clusters. In: Proceedings of 17th International Conference on Machine Learning, pp. 727–730. Morgan Kaufmann, San Francisco (2000)

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