NDLI: OPTICS:ordering points to identify the clustering structure

Content Provider	ACM Digital Library
Author	Breunig, Markus M. Sander, Jörg Ankerst, Mihael Kriegel, Hans-Peter
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.
Starting Page	49
Ending Page	60
Page Count	12
File Format	PDF
ISSN	01635808
DOI	10.1145/304181.304187
Journal	ACM SIGMOD Record (SGMD)
Volume Number	28
Issue Number	2
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1999-06-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Cluster analysis Visualization Database mining
Content Type	Text
Resource Type	Article
Subject	Information Systems Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

OPTICS: ordering points to identify the clustering structure

OPTICS: Ordering Points To Identify the Clustering Structure (1999)

OPTICS: Ordering Points To Identify the Clustering Structure (1999)

OPTICS: Ordering points to identify the clustering structure (1999)

Data bubbles:quality preserving performance boosting for hierarchical clustering

Applying database visualization to the World Wide Web

Interactive data analysis: the Control project

Delaunay:a database visualization system

Automatically Determining the Number of Clusters in Unlabeled Data Sets

OPTICS:ordering points to identify the clustering structure

Similar Documents

OPTICS: ordering points to identify the clustering structure

OPTICS: Ordering Points To Identify the Clustering Structure (1999)

OPTICS: Ordering Points To Identify the Clustering Structure (1999)

OPTICS: Ordering points to identify the clustering structure (1999)

Data bubbles:quality preserving performance boosting for hierarchical clustering

Applying database visualization to the World Wide Web

Interactive data analysis: the Control project

Delaunay:a database visualization system

Automatically Determining the Number of Clusters in Unlabeled Data Sets

OPTICS:ordering points to identify the clustering structure