Name
Abstract | ||
|---|---|---|
For applications of clustering algorithms, a key technique is to handle complicatedly distributed clusters effectively and efficiently. On the basis of analysis and research of traditional clustering algorithms, a clustering algorithm based on density and adaptive density-reachable is presented in this paper. Experimental results show that the algorithm can handle clusters of arbitrary shapes, sizes and densities. At the same time, the algorithm can evidently reduce time and space complexity as compared with other density-based algorithms. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/CSSE.2008.381 | CSSE (4) |
Keywords | Field | DocType |
traditional clustering algorithm,adaptive density-reachable,arbitrary clusters,clustering algorithm,key technique,density-based algorithm,space complexity,arbitrary shape,time complexity,clustering algorithms,computer science,software engineering,computational complexity | Fuzzy clustering,CURE data clustering algorithm,Computer science,Artificial intelligence,Cluster analysis,Distributed computing,Canopy clustering algorithm,Data stream clustering,Correlation clustering,Pattern recognition,Determining the number of clusters in a data set,Constrained clustering,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.44 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hai-Dong Meng | 1 | 2 | 0.81 |
| Yuchen Song | 2 | 4 | 3.33 |
| Fei-Yan Song | 3 | 1 | 0.44 |