摘要：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 requirements of domain knowledge to
determine the input parameters, discovery of clusters with arbitrary shape and good
efficiency on large databases. The well-known clustering algorithms offer no solution
to the combination of these requirements. In this paper, a density based clustering
algorithm is presented relying on a knowledge acquired from the data which is
designed to discover clusters of arbitrary shape. The proposed algorithm requires no
input parameter. We performed an experimental evaluation of the efficiency of it using
real and synthetic data. The results of our experiments demonstrate that the proposed
algorithm is significantly efficient in discovering clusters of arbitrary shape and size.
