期刊
KNOWLEDGE-BASED SYSTEMS
卷 187, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.knosys.2019.06.032
关键词
Density peak; FastDPeak; kNN-density
资金
- National Science Foundation of China [61673186, 71771094, 61876068]
- Project of science and technology plan of Fujian Province of China [2017H01010065, 2019H01010129]
- Quanzhou City Science AMP
- Technology Program of China [2018C114R, 2018C110R]
- Natural Foundation Key Program for Young Scholars in the Universities of Fujian Province, China [JZ160409]
- State Key Laboratory of Integrated Services Networks, Xidian University, China [ISN20-11]
- open project of Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, China [KJS1839]
Density Peak (DPeak) clustering algorithm is not applicable for large scale data, due to two quantities, i.e, rho and delta, are both obtained by brute force algorithm with complexity O(n(2)). Thus, a simple but fast DPeak, namely FastDPeak,(1) is proposed, which runs in about O(nlog(n)) expected time in the intrinsic dimensionality. It replaces density with kNN-density, which is computed by fast kNN algorithm such as cover tree, yielding huge improvement for density computations. Based on kNN-density, local density peaks and non-local density peaks are identified, and a fast algorithm, which uses two different strategies to compute delta for them, is also proposed with complexity O(n). Experimental results show that FastDPeak is effective and outperforms other variants of DPeak. (C) 2019 Elsevier B.V. All rights reserved.
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