Name
Abstract | ||
|---|---|---|
Affinity propagation clustering (AP) has two limitations: it is hard to know
what value of parameter 'preference' can yield an optimal clustering solution,
and oscillations cannot be eliminated automatically if occur. The adaptive AP
method is proposed to overcome these limitations, including adaptive scanning
of preferences to search space of the number of clusters for finding the
optimal clustering solution, adaptive adjustment of damping factors to
eliminate oscillations, and adaptive escaping from oscillations when the
damping adjustment technique fails. Experimental results on simulated and real
data sets show that the adaptive AP is effective and can outperform AP in
quality of clustering results. |
Year | Venue | Keywords |
|---|---|---|
2008 | Clinical Orthopaedics and Related Research | large number of clusters,adaptive clustering,affinity propagation(ap)clustering,artificial intelligent,affinity propagation,oscillations,search space |
Field | DocType | Volume |
Cluster (physics),Canopy clustering algorithm,CURE data clustering algorithm,Data set,Mathematical optimization,Correlation clustering,Affinity propagation,FLAME clustering,Cluster analysis,Mathematics | Journal | abs/0805.1 |
ISSN | Citations | PageRank |
K. Wang, J. Zhang, D. Li, X. Zhang and T. Guo. Adaptive Affinity
Propagation Clustering. Acta Automatica Sinica, 33(12):1242-1246, 2007 | 16 | 1.02 |
References | Authors | |
2 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kaijun Wang | 1 | 70 | 4.86 |
| Junying Zhang | 2 | 153 | 21.12 |
| Dan Li | 3 | 18 | 2.39 |
| Xinna Zhang | 4 | 16 | 1.02 |
| Tao Guo | 5 | 386 | 45.64 |