Name
Abstract | ||
|---|---|---|
Traditional grid-density based spatial clustering algorithms divide input data space into partitions with same width and neglect the natural distributing character of initial data space. A new robust spatial clustering algorithm based on optimized-division (OpD-Clus) is proposed in this paper. Spatial data space is divided by hyper-planes which are encertained with axis-paralleled histogram in OpD- Clus algorithm. Division of data space relies on natual distributing character of input data space to improve the accuracy and efficiency of spatial clustering. Simultaneity, the outstanding difference between density-region and spare-region makes setting of density threshold parameter easily and reduces the parameter dependence of spatial clustering algorithm. The validity, efficiency and un-sensitivity of paramenters of OpD-Clus algorithm is demonstrated by experiment results. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/FSKD.2007.525 | FSKD (2) |
Keywords | Field | DocType |
spatial data | Canopy clustering algorithm,Fuzzy clustering,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Pattern recognition,Computer science,Determining the number of clusters in a data set,Artificial intelligence,Cluster analysis,Machine learning,DBSCAN | Conference |
Volume | Issue | ISBN |
2 | null | 0-7695-2874-0 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhang Jianpei | 1 | 83 | 21.93 |
| Yue Yang | 2 | 0 | 1.01 |
| Jing Yang | 3 | 77 | 7.66 |
| Ze-bao Zhang | 4 | 0 | 0.34 |
| Zhuo Liu | 5 | 118 | 16.03 |