Relational Clustering

Hammer, Barbara; Hasenfuss, Alexander

doi:10.4230/DagSemProc.07131.6

Document

Relational Clustering

Authors Barbara Hammer, Alexander Hasenfuss

Part of: Volume: Dagstuhl Seminar Proceedings, Volume 7131
Part of: Series: Dagstuhl Seminar Proceedings (DagSemProc)
License: Creative Commons Attribution 4.0 International license
Publication Date: 2007-07-16

PDF

File

PDF

DagSemProc.07131.6.pdf

Filesize: 200 kB
15 pages

Document Identifiers

DOI: 10.4230/DagSemProc.07131.6
URN: urn:nbn:de:0030-drops-11182

Subject Classification

Keywords

Neural gas
dissimilarity data

Metrics

Access Statistics
Total Accesses (updated on a weekly basis)

0

PDF Downloads

0

Metadata Views

Abstract

We introduce relational variants of neural gas, a very efficient and
powerful neural clustering algorithm. It is assumed that a similarity or
dissimilarity matrix is given which stems from Euclidean distance or dot
product, respectively, however, the underlying embedding of points is unknown.
In this case, one can equivalently formulate batch optimization in
terms of the given similarities or dissimilarities, thus providing a way to
transfer batch optimization to relational data. Interestingly, convergence
is guaranteed even for general symmetric and nonsingular metrics.

Cite As Get BibTex

Barbara Hammer and Alexander Hasenfuss. Relational Clustering. In Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings, Volume 7131, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007) https://doi.org/10.4230/DagSemProc.07131.6

Author Details

Barbara Hammer

Alexander Hasenfuss

Questions / Remarks / Feedback

Feedback for Dagstuhl Publishing

Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail