Name
Abstract | ||
|---|---|---|
Conventional semi-supervised clustering approaches have several shortcomings, such as (1) not fully utilizing all useful must-link and cannot-link constraints, (2) not considering how to deal with high dimensional data with noise, and (3) not fully addressing the need to use an adaptive process to further improve the performance of the algorithm. In this paper, we first propose the transitive clos... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TKDE.2017.2695615 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | Field | DocType |
Clustering algorithms,Measurement,Algorithm design and analysis,Matrix decomposition,Cancer,Bioinformatics,Kernel | Kernel (linear algebra),Data mining,Local consistency,Clustering high-dimensional data,Subspace topology,Affinity propagation,Computer science,Artificial intelligence,Constrained clustering,Cluster analysis,Transitive closure,Machine learning | Journal |
Volume | Issue | ISSN |
29 | 8 | 1041-4347 |
Citations | PageRank | References |
7 | 0.46 | 58 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiwen Yu | 1 | 65 | 10.06 |
| Zongqiang Kuang | 2 | 7 | 0.46 |
| Jiming Liu | 3 | 3241 | 312.47 |
| Hongsheng Chen | 4 | 28 | 1.06 |
| Jun Zhang | 5 | 2491 | 127.27 |
| Jane You | 6 | 1885 | 136.93 |
| Hau-San Wong | 7 | 1008 | 86.89 |
| Guoqiang Han | 8 | 439 | 43.27 |