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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/809128

Title: Cayley graphs as classifiers for data mining: the influence of asymmetries
Author/Creator: Kelarev, Andrei; Ryan, Joe; Yearwood, John
Institution: The University of Newcastle. Faculty of Engineering & Built Environment, School of Electrical Engineering and Computer Science
Description: The endomorphism monoids of graphs have been actively investigated. They are convenient tools expressing asymmetries of the graphs. One of the most important classes of graphs considered in this framework is that of Cayley graphs. Our paper proposes a new method of using Cayley graphs for classification of data. We give a survey of recent results devoted to the Cayley graphs also involving their endomorphism monoids.
Relation: Discrete Mathematics Vol. 309, Issue 17, p. 5360-5369
Publisher Link: http://dx.doi.org/10.1016/j.disc.2008.11.030
Date: 2009
Publisher: Elsevier
Keyword(s): Cayley graphs; endomorphism monoids; classification
Resource Type: journal article
Identifier: http://hdl.handle.net/1959.13/809128
Identifier: ISSN:0012-365X

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