Name
Abstract | ||
|---|---|---|
In pattern recognition and data mining, clustering is a classical technique to group matters of interest and has been widely employed to numerous applications. Among various clustering algorithms, K-means (KM) clustering is most popular for its simplicity and efficiency. However, with the rapid development of the social network, high-dimensional data are frequently generated, which poses a conside... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TNNLS.2018.2850823 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Clustering algorithms,Robustness,Linear programming,Dimensionality reduction,Adaptation models,Manifolds,Adaptive learning | Dimensionality reduction,Subspace topology,Pattern recognition,Computer science,Robustness (computer science),Curse of dimensionality,Artificial intelligence,Cluster analysis,Adaptive learning,Discriminative model,Centroid | Journal |
Volume | Issue | ISSN |
30 | 3 | 2162-237X |
Citations | PageRank | References |
7 | 0.41 | 25 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaodong Wang | 1 | 35 | 5.19 |
| Rung-Ching Chen | 2 | 331 | 37.37 |
| Zhiqiang Zeng | 3 | 139 | 16.35 |
| Chaoqun Hong | 4 | 324 | 13.19 |
| Fei Yan | 5 | 28 | 9.01 |