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Clustering with Repulsive Prototypes

Winkler, Roland and Rehm, Frank and Kruse, Rudolf (2008) Clustering with Repulsive Prototypes. In: Advances in Data Analysis, Data Handling and Business Intelligence, pp. 207-215. Springer Berlin Heidelberg. GfKL 2008, 2008-07-16 - 2008-07-18, Hamburg. ISBN 978-3-642-01045-3 ISSN 1431-8814


Official URL: http://www.springerlink.com/content/l747q41ll224v6p1/


Although there is no exact definition for the term extit{cluster}, in the 2D case, it is fairly easy for human beings to decide which objects belong together. For machines on the other hand, it is hard to determine which objects form a cluster. Depending on the problem, the success of a clustering algorithm depends on the idea of their creators about what a cluster should be. Likewise, each clustering algorithm comprises a characteristic idea of the term cluster. For example the fuzzy c-means algorithm tends to find spherical clusters with equal numbers of objects. Noise clustering focuses on finding spherical clusters of user-defined diameter. In this paper, we present an extension to noise clustering that tries to maximize the distances between prototypes. For that purpose, the prototypes behave like repulsive magnets that have an inertia depending on their sum of membership values. Using this repulsive extension, it is possible to prevent that groups of objects are divided into more than one cluster. Due to the repulsion and inertia, we show that it is possible to determine the number and approximate position of clusters in a data set.

Item URL in elib:https://elib.dlr.de/56162/
Document Type:Conference or Workshop Item (Speech, Paper)
Title:Clustering with Repulsive Prototypes
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Date:31 August 2008
Journal or Publication Title:Advances in Data Analysis, Data Handling and Business Intelligence
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 207-215
Publisher:Springer Berlin Heidelberg
Series Name:Studies in Classification, Data Analysis, and Knowledge Organization
Keywords:Repulsive Prototypes, Fuzzy c-Means, Noise Clustering, Air Traffic Management
Event Title:GfKL 2008
Event Location:Hamburg
Event Type:international Conference
Event Dates:2008-07-16 - 2008-07-18
Organizer:German Classification Society - Gesellschaft für Klassifikation (GfKl)
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:ATM and Operation (old)
DLR - Research area:Aeronautics
DLR - Program:L AO - Air Traffic Management and Operation
DLR - Research theme (Project):L - Effiziente Flugführung und Flugbetrieb (old)
Location: Braunschweig
Institutes and Institutions:Institute of Flight Control > Air traffic systems
Deposited By: Winkler, Roland
Deposited On:02 Apr 2009
Last Modified:31 Jul 2019 19:23

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