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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 ...
An Initialization Method for the K-means Algorithm using RNN and Coupling Degree
(Foundation of Computer Science (FCS), 2011)
Since K-means is widely used for general clustering, its performance is a critical point. This performance depends highly on initial cluster centers since it may converge to numerous local minima. In this paper a proposed ...
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 ...