A Density Peaks Clustering Algorithm With Sparse Search and K-d Tree
Author:
Affiliation:
1. School of Science, Harbin University of Science and Technology, Harbin, China
2. School of Materials Science and Engineering, Harbin University of Science and Technology, Harbin, China
Funder
National Natural Science Foundation of China
State Key Laboratory of Solidification Processing in Northwestern Polytechnical University
Heilongjiang Postdoctoral Fund for Scientific Research Initiation
Fundamental Research Foundation for Universities of Heilongjiang Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/9668973/09829753.pdf?arnumber=9829753
Reference50 articles.
1. Effective Density Peaks Clustering Algorithm Based on the Layered K-Nearest Neighbors and Subcluster Merging
2. Density decay graph-based density peak clustering
3. Active learning through density clustering
4. A fast density-based data stream clustering algorithm with cluster centers self-determined for mixed data
5. Multidimensional binary search trees used for associative searching
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