Name
Abstract | ||
|---|---|---|
Density based clustering techniques like DBSCAN can find arbitrary shaped clusters along with noisy outliers. A severe drawback of the method is its huge time requirement which makes it a unsuitable one for large data sets. One solution is to apply DBSCAN using only a few selected prototypes. But because of this the clustering result can deviate from that which uses the full data set. A novel method proposed in the paper is to use two types of prototypes, one at a coarser level meant to reduce the time requirement, and the other at a finer level meant to reduce the deviation of the result. Prototypes are derived using leaders clustering method. The proposed hybrid clustering method called l-DBSCAN is analyzed and experimentally compared with DBSCAN which shows that it could be a suitable one for large data sets. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICPR.2006.741 | Pattern Recognition, 2006. ICPR 2006. 18th International Conference |
Keywords | Field | DocType |
noise,pattern clustering,fast hybrid density based clustering,l-DBSCAN,leaders clustering method,noisy outliers,shaped clusters | OPTICS algorithm,Clustering high-dimensional data,CURE data clustering algorithm,Pattern recognition,Correlation clustering,Computer science,Determining the number of clusters in a data set,SUBCLU,Artificial intelligence,Cluster analysis,DBSCAN | Conference |
Volume | ISSN | ISBN |
1 | 1051-4651 | 0-7695-2521-0 |
Citations | PageRank | References |
30 | 1.35 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. Viswanath | 1 | 148 | 11.77 |
| Rajwala Pinkesh | 2 | 30 | 1.35 |