Now showing items 1-3 of 3
A dynamic method for discovering density varied clusters
Density-based spatial clustering of applications with noise (DBSCAN) is a base algorithm for density based clustering. It can find out the clusters of different shapes and sizes from a large amount of data, which is ...
MULTI-DENSITY DBSCAN USING REPRESENTATIVES: MDBSCAN-UR.
DBSCAN is one of the most popular algorithms for cluster analysis. It can discover clusters with arbitrary shape and separate noises. But this algorithm cannot choose its parameter according to distributing of dataset. It ...
Multi density DBSCAN
(Springer, Berlin, Heidelberg, 2011)
Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal ...