@inproceedings{Ester1996,
title = {A density-based algorithm for discovering clusters in large spatial
databases with noise},
author = {Martin Ester and Hans-Peter Kriegel and J{\"o}rg Sander and Xiaowei Xu},
booktitle = {International Conference on Knowledge Discovery and Data Mining},
pages = {226--231},
url = {http://ifsc.ualr.edu/xwxu/publications/kdd-96.pdf},
year = {1996},
abstract = {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, we present
the new clustering algorithm DBSCAN relying on a density-based notion
of clusters which is designed to discover clusters of arbitrary shape.
DBSCAN requires only one input parameter and supports the user in
determining an appropriate value for it. We performed an experimental
evaluation of the effectiveness and efficiency of DBSCAN using synthetic
data and real data of the SEQUOIA 2000 benchmark. The results of
our experiments demonstrate that (1) DBSCAN is significantly more
effective in discovering clusters of arbitrary shape than the well-known
algorithm CLARANS, and that (2) DBSCAN outperforms CLARANS by a factor
of more than 100 in terms of efficiency.},
keywords = {Clustering }
}
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