Name
Abstract | ||
|---|---|---|
This paper presents a novel unsupervised robust clustering technique based on the Gravitational Law and the second Newton's motion Law. The technique automatically determines the number of clusters in the target data set. Basically, each data record in the source data set is considered as an object in the feature space. The objects are moved by using the gravitational force and the second motion law. Because the clusters define a disjoint collection of sets, an optimal disjoint set union-find structure is used to store and update the clusters that are being conformed by closest objects. The proposed technique is robust to noise, can be used to generate a partition of the data set at multiple resolution levels, and can also be used to extract seeds to form a good summary of the data. Experiments with synthetic and real data were conducted to show the performance of the proposed clustering technique. |
Year | Venue | Keywords |
|---|---|---|
2003 | SIAM Proceedings Series | clustering,gravitational,robust,unsupervised,scalable |
Field | DocType | Citations |
k-medians clustering,Canopy clustering algorithm,CURE data clustering algorithm,Pattern recognition,Correlation clustering,Computer science,Determining the number of clusters in a data set,Artificial intelligence,Nearest-neighbor chain algorithm,Cluster analysis,Single-linkage clustering | Conference | 31 |
PageRank | References | Authors |
1.20 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonatan Gómez | 1 | 241 | 29.70 |
| Dipankar Dasgupta | 2 | 1226 | 121.74 |
| Olfa Nasraoui | 3 | 1515 | 164.53 |