Name
Abstract | ||
|---|---|---|
The dominant sets clustering algorithm has some interesting properties and has achieved impressive results in experiments. However, with the data represented as feature vectors, we need to estimate data similarity and the regularization parameter influences the clustering results and number of clusters significantly. To obtain a specified number of clusters efficiently with the dominant sets algorithm, we present a target dominant set clustering algorithm. Our algorithm detects clusters in the first step, and then extracts dominant sets around the cluster centers based on a specially designed game dynamics. In addition, we show that this game dynamics can be utilized to reduce the computation and memory load significantly. Experiments show that our algorithm performs favorably to the original dominant sets algorithm in clustering quality with much smaller computation load than the latter. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-01421-6_28 | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT II |
Keywords | Field | DocType |
Clustering, Dominant set, Cluster center | Cluster (physics),Feature vector,Pattern recognition,Computer science,Game dynamics,Regularization (mathematics),Artificial intelligence,Cluster analysis,Computation | Conference |
Volume | ISSN | Citations |
11140 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Hou | 1 | 126 | 17.11 |
| Chengcong Lv | 2 | 0 | 1.01 |
| Aihua Zhang | 3 | 23 | 9.63 |
| Xu E | 4 | 4 | 3.89 |