Name
Abstract | ||
|---|---|---|
K-means clustering is one of the most popular clustering algorithms and has been embedded in other clustering algorithms, e.g. the last step of spectral clustering. In this paper, we propose two techniques to improve previous k-means clustering algorithm by designing two different adjacent matrices. Extensive experiments on public UCI datasets showed the clustering results of our proposed algorithms significantly outperform three classical clustering algorithms in terms of different evaluation metrics. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11042-019-08009-x | Multimedia Tools and Applications |
Keywords | Field | DocType |
k-means clustering, Similarity measurement, Adjacent matrix, Unsupervised learning | Spectral clustering,k-means clustering,Pattern recognition,Matrix (mathematics),Computer science,Unsupervised learning,Artificial intelligence,Cluster analysis | Journal |
Volume | Issue | ISSN |
78 | 23 | 1380-7501 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jukai Zhou | 1 | 0 | 0.34 |
| Tong Liu | 2 | 47 | 12.77 |
| Jingting Zhu | 3 | 0 | 0.34 |