Name
Abstract | ||
|---|---|---|
Unsupervised clustering algorithm is successfully applied in many fields. While the method of fast search and find of density peaks can efficiently discover the centers of clusters by finding the high-density peaks, it suffers from selecting the cluster center manually which depends legitimately on subjective experience. This paper presents a novel effective clustering method for finding density peaks (ECDP). We harness statistics-based methods with geometric features to attain the density peaks automatically and accurately. Our studies demonstrate that our approach can select the cluster center efficiently and effectively for massive datasets. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BDCloud.2018.00020 | 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom) |
Keywords | Field | DocType |
CFSFDP,Hanoi Tower,Unsupervised clustering algorithm,Weighted least square method | Cluster (physics),Pattern recognition,Computer science,Human–computer interaction,Artificial intelligence,Cluster analysis,Weighted least squares method | Conference |
ISSN | ISBN | Citations |
2158-9178 | 978-1-7281-1141-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui-dong Qi | 1 | 0 | 0.34 |
| Jiantao Zhou | 2 | 2 | 3.41 |
| Xiaoyu Song | 3 | 318 | 46.99 |