Cluster analysis is a primary method for database
mining. It is either used as a stand-alone tool to get
insight into the distribution of a data set, e.g. to
focus further analysis and data processing, or as a
preprocessing step for other algorithms operating on
the detected clusters. Almost all of the well-known
clustering algorithms require input parameters which
are hard to determine but have a significant influence
on the clustering result. Furthermore, for many
real-data sets there does not even exist a global
parameter setting for which the result of the
clustering algorithm describes the intrinsic clustering
structure accurately. We introduce a new algorithm for
the purpose of cluster analysis which does not produce
a clustering of a data set explicitly; but instead
creates an augmented ordering of the database
representing its density-based clustering structure.
This cluster-ordering contains information which is
equivalent to the density-based clusterings
corresponding to a broad range of parameter settings.
It is a versatile basis for both automatic and
interactive cluster analysis. We show how to
automatically and efficiently extract not only
'traditional' clustering information (e.g.
representative points, arbitrary shaped clusters), but
also the intrinsic clustering structure. For medium
sized data sets, the cluster-ordering can be
represented graphically and for very large data sets,
we introduce an appropriate visualization technique.
Both are suitable for interactive exploration of the
intrinsic clustering structure offering additional
insights into the distribution and correlation of the
data.
%0 Journal Article
%1 ankerst-optics-1999
%A Ankerst, Mihael
%A Breunig, Markus M.
%A Kriegel, Hans-Peter
%A Sander, J?rg
%C New York, NY, USA
%D 1999
%I ACM
%J ACM SIGMOD Record
%K clustering dynamic
%N 2
%P 49--60
%R http://doi.acm.org/10.1145/304181.304187
%T OPTICS: Ordering Points to Identify the Clustering
Structure
%U http://portal.acm.org/citation.cfm?id=304187
%V 28
%X Cluster analysis is a primary method for database
mining. It is either used as a stand-alone tool to get
insight into the distribution of a data set, e.g. to
focus further analysis and data processing, or as a
preprocessing step for other algorithms operating on
the detected clusters. Almost all of the well-known
clustering algorithms require input parameters which
are hard to determine but have a significant influence
on the clustering result. Furthermore, for many
real-data sets there does not even exist a global
parameter setting for which the result of the
clustering algorithm describes the intrinsic clustering
structure accurately. We introduce a new algorithm for
the purpose of cluster analysis which does not produce
a clustering of a data set explicitly; but instead
creates an augmented ordering of the database
representing its density-based clustering structure.
This cluster-ordering contains information which is
equivalent to the density-based clusterings
corresponding to a broad range of parameter settings.
It is a versatile basis for both automatic and
interactive cluster analysis. We show how to
automatically and efficiently extract not only
'traditional' clustering information (e.g.
representative points, arbitrary shaped clusters), but
also the intrinsic clustering structure. For medium
sized data sets, the cluster-ordering can be
represented graphically and for very large data sets,
we introduce an appropriate visualization technique.
Both are suitable for interactive exploration of the
intrinsic clustering structure offering additional
insights into the distribution and correlation of the
data.
@article{ankerst-optics-1999,
abstract = {Cluster analysis is a primary method for database
mining. It is either used as a stand-alone tool to get
insight into the distribution of a data set, e.g. to
focus further analysis and data processing, or as a
preprocessing step for other algorithms operating on
the detected clusters. Almost all of the well-known
clustering algorithms require input parameters which
are hard to determine but have a significant influence
on the clustering result. Furthermore, for many
real-data sets there does not even exist a global
parameter setting for which the result of the
clustering algorithm describes the intrinsic clustering
structure accurately. We introduce a new algorithm for
the purpose of cluster analysis which does not produce
a clustering of a data set explicitly; but instead
creates an augmented ordering of the database
representing its density-based clustering structure.
This cluster-ordering contains information which is
equivalent to the density-based clusterings
corresponding to a broad range of parameter settings.
It is a versatile basis for both automatic and
interactive cluster analysis. We show how to
automatically and efficiently extract not only
'traditional' clustering information (e.g.
representative points, arbitrary shaped clusters), but
also the intrinsic clustering structure. For medium
sized data sets, the cluster-ordering can be
represented graphically and for very large data sets,
we introduce an appropriate visualization technique.
Both are suitable for interactive exploration of the
intrinsic clustering structure offering additional
insights into the distribution and correlation of the
data.},
added-at = {2011-10-18T11:05:20.000+0200},
address = {New York, NY, USA},
author = {Ankerst, Mihael and Breunig, Markus M. and Kriegel, Hans-Peter and Sander, J?rg},
biburl = {https://www.bibsonomy.org/bibtex/286b1a51b501c882f9a4f1cdacca3f7ed/mhwombat},
doi = {http://doi.acm.org/10.1145/304181.304187},
interhash = {7417e17c0e8eec9f1a9f2bc57a476b15},
intrahash = {86b1a51b501c882f9a4f1cdacca3f7ed},
issn = {0163-5808},
journal = {ACM SIGMOD Record},
keywords = {clustering dynamic},
number = 2,
pages = {49--60},
publisher = {ACM},
timestamp = {2016-07-12T19:25:30.000+0200},
title = {{OPTICS}: Ordering Points to Identify the Clustering
Structure},
url = {http://portal.acm.org/citation.cfm?id=304187},
volume = 28,
year = 1999
}