A density-based algorithm for discovering clusters in large spatial databases with noise

DMCA

A density-based algorithm for discovering clusters in large spatial databases with noise (1996)

Cached

Download Links

Other Repositories/Bibliography

DBLP

by Martin Ester , Hans-Peter Kriegel , Jörg Sander , Xiaowei Xu

Citations:

1714 - 68 self

BibTeX

@INPROCEEDINGS{Ester96adensity-based,
    author = {Martin Ester and Hans-Peter Kriegel and Jörg Sander and Xiaowei Xu},
    title = {A density-based algorithm for discovering clusters in large spatial databases with noise},
    booktitle = {},
    year = {1996},
    pages = {226--231},
    publisher = {AAAI Press}
}

OpenURL

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 CLAR-ANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.

Keyphrases

large spatial database arbitrary shape density-based algorithm input parameter domain knowledge synthetic data class identification density-based notion appropriate value real data spatial database following requirement well-known clustering algorithm well-known algorithm clar-ans algorithm dbscan relying experimental evaluation minimal requirement good efficiency large database dbscan outperforms clarans

DMCA