Name
Abstract | ||
|---|---|---|
We present a novel algorithm for clustering streams of multidimensional points based on kernel density estimates of the data. The algorithm requires only one pass over each data point and a constant amount of space, which depends only on the accuracy of clustering. The algorithm recognizes clusters of nonspherical shapes and handles both inserted and deleted objects in the input stream. Querying the membership of a point in a cluster can be answered in constant time. |
Year | Venue | Keywords |
|---|---|---|
2006 | ECAI | multidimensional point,input stream,constant amount,clustering stream,kernel density estimate,data point,constant time,novel algorithm,kernel density estimation,nonspherical shape |
Field | DocType | Volume |
Canopy clustering algorithm,Fuzzy clustering,CURE data clustering algorithm,Data stream clustering,Pattern recognition,Correlation clustering,Computer science,FLAME clustering,Artificial intelligence,Cluster analysis,Variable kernel density estimation | Conference | 141 |
ISSN | ISBN | Citations |
0922-6389 | 1-58603-642-4 | 1 |
PageRank | References | Authors |
0.37 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefano Lodi | 1 | 231 | 21.96 |
| G. Moro | 2 | 192 | 16.25 |
| Claudio Sartori | 3 | 136 | 17.26 |