A novel DBSCAN with entropy and probability for mixed data-Reference-Cited by-同舟云学术

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A novel DBSCAN with entropy and probability for mixed data

Published:2017-03-17 Issue:2 Volume:20 Page:1313-1323
ISSN:1386-7857
Container-title:Cluster Computing
language:en
Short-container-title:Cluster Comput

Author:

Liu Xingxing,Yang Qing,He Ling

Funder

Major Projects of the National Social Science Fund of China

National Natural Science Foundation of China

Junior Fellowships for CAST Advanced Innovation Think-tank Program

China Postdoctoral Science Foundation Funded Project

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Software

Link

http://link.springer.com/article/10.1007/s10586-017-0818-3/fulltext.html

Reference33 articles.

1. Hsu, C.C., Huang, Y.P.: Incremental clustering of mixed data based on distance hierarchy. Expert Syst. Appl. 35(3), 1177–1185 (2008)

2. Zhang, X., Wu, Y., Zhao, C.: MrHeter: improving MapReduce performance in heterogeneous environments. Clust. Comput. 19, 1691–1701 (2016)

3. Kaur, A., Datta, A.: A novel algorithm for fast and scalable subspace clustering of high-dimensional data. J. Big Data 2(1), 1–24 (2015)

4. Dutta, D., Dutta, P., Sil, J.: Simultaneous feature selection and clustering with mixed features by multi objective genetic algorithm. Int. J. Hybrid Intell. Syst. 11(1), 41–54 (2014)

5. Sakr, S.: Cloud-hosted databases: technologies, challenges and opportunities. Clust. Comput. 17(2), 87–502 (2014)

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