Name
Abstract | ||
|---|---|---|
This paper introduces an incremental data clustering algorithm based on the gravitational law. Basically, data samples are considered as unit-mass particles exposed to gravitational forces. Data points are clustered according their proximity during the simulation of the dynamical system defined by their gravitational fields. When the simulation is stopped, a set of prototypes is generated (several prototypes per cluster found). Each prototype will have associated a mass that is proportional to the number of particles in the sub-cluster and will be used as additional particle when new data samples are given for clustering. Experiments are performed on synthetic data sets and the obtained results are presented. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-76631-5_44 | MICAI |
Keywords | Field | DocType |
additional particle,incremental data,new data sample,data sample,gravitational clustering,dynamical system,gravitational law,data point,synthetic data set,incremental approach,gravitational force,gravitational field,dynamic system,synthetic data,data clustering | k-medians clustering,Data point,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Gravitational field,Computer science,Artificial intelligence,Cluster analysis,Gravitation,Machine learning | Conference |
Volume | ISSN | ISBN |
4827 | 0302-9743 | 3-540-76630-8 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonatan Gomez | 1 | 16 | 3.01 |
| Juan Peña-Kaltekis | 2 | 0 | 0.34 |
| Nestor Romero-Leon | 3 | 0 | 0.34 |
| Elizabeth Leon | 4 | 33 | 5.26 |