Name
Abstract | ||
|---|---|---|
In recent years, the uncertain data stream which is related in many real applications attracts more and more attention of researchers. As one aspect of uncertain character, existence-uncertainty can affect the clustering process and results significantly. The lately reported clustering algorithms are all based on K-Means algorithm with the inhere shortage. DCUStream algorithm which is density-based clustering algorithm over uncertain data stream is proposed in this paper. It can find arbitrary shaped clusters with less time cost in high dimension data stream. In the meantime, a dynamic density threshold is designed to accommodate the changing density of grids with time in data stream. The experiment results show that DCUStream algorithm can acquire more accurate clustering result and execute the clustering process more efficiently on progressing uncertain data stream. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/FSKD.2012.6233800 | FSKD |
Keywords | Field | DocType |
density-based,pattern clustering,high dimension data stream,existence-uncertainty,uncertain data stream,dcustream algorithm,k-means algorithm,arbitrary shaped clusters,uncertain data streams processing,dynamic density threshold,dynamic density-based clustering algorithm,grid density,clustering algorithms,uncertainty,wireless sensor networks,k means algorithm,algorithm design and analysis | Canopy clustering algorithm,Fuzzy clustering,Data mining,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Computer science,Determining the number of clusters in a data set,Artificial intelligence,Constrained clustering,Cluster analysis,Machine learning | Conference |
Volume | Issue | ISBN |
null | null | 978-1-4673-0025-4 |
Citations | PageRank | References |
4 | 0.38 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Yang | 1 | 28 | 12.09 |
| Zhuo Liu | 2 | 118 | 16.03 |
| Zhang Jianpei | 3 | 83 | 21.93 |
| Jing Yang | 4 | 37 | 20.88 |