Name
Abstract | ||
|---|---|---|
The traditional clustering algorithms are only suitable for the static datasets. As for the dynamic and incremental datasets, the clustering results will become unreliable after data updates, and also it will certainly decrease efficiency and waste computing resources to cluster all of the data again. To overcome these problems, a new incremental clustering algorithm is proposed on the basis of density and density-reachable. Theoretical analysis and experimental results demonstrate that the incremental algorithm can improve the efficiency of data resource utilization, and handle the dynamic datasets effectively. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/NCM.2009.376 | NCM |
Keywords | Field | DocType |
traditional clustering algorithm,incremental algorithm,new incremental clustering algorithm,incremental datasets,static datasets,clustering result,data updates,dynamic datasets,density reachable,data resource utilization,algorithm design and analysis,computational complexity,resource utilization,statistical analysis,clustering algorithm,cluster analysis,clustering algorithms,shape | Data mining,Canopy clustering algorithm,Clustering high-dimensional data,CURE data clustering algorithm,Data stream clustering,Algorithm design,Correlation clustering,Computer science,Artificial intelligence,Constrained clustering,Cluster analysis,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.37 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuchen Song | 1 | 4 | 3.33 |
| Hai-Dong Meng | 2 | 2 | 0.81 |
| Shu-Ling Wang | 3 | 1 | 0.37 |
| M.J. O’Grady | 4 | 209 | 19.33 |
| Gregory O'Hare | 5 | 22 | 3.80 |