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Machine Learning

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Elkan’s k-Means Algorithm for Graphs
Citation key Jain2010b
Author Jain, B. and Obermayer, K.
Title of Book Advances in Soft Computing
Pages 22-32
Year 2010
ISBN 978-3-642-16772-0, 978-3-642-16773-7
ISSN 0302-9743
DOI 10.1007/978-3-642-16773-7_2
Volume 6438
Note 9th Mexican International Conference on Artificial Intelligence, MICAI 2010, Pachuca, Mexico, November 8-13, 2010, Proceedings, Part II
Editor Sidorov, Grigori and Hernández Aguirre, Arturo and Reyes García, CarlosAlberto
Publisher Springer Berlin Heidelberg
Series Lecture Notes in Computer Science
Abstract This paper proposes a fast k-means algorithm for graphs based on Elkan’s k-means for vectors. To accelerate the k-means algorithm for graphs without trading computational time against solution quality, we avoid unnecessary graph distance calculations by exploiting the triangle inequality of the underlying distance metric. In experiments we show that the accelerated k-means for graphs is faster than k-means for graphs provided there is a cluster structure in the data.
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