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Evaluating the Quality of Clustering Algorithms Using Cluster Path Lengths / F. Zaidi; D. Archambault; G. Melançon; Daniel Archambault

Advances in Data Mining. Applications and Theoretical Aspects, Volume: 6171 LNAI, Pages: 42 - 56

Swansea University Author: Daniel, Archambault

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DOI (Published version): 10.1007/978-3-642-14400-4_4

Published in: Advances in Data Mining. Applications and Theoretical Aspects
ISBN: 978-3-642-14399-1 978-3-642-14400-4
Published: 2010
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Item Description: @article archambault2010,title = Evaluating the quality of clustering algorithms using cluster path lengths,journal = Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),year = 2010,volume = 6171 LNAI,pages = 42-56,author = Zaidi, F. and Archambault, D. and Melançon, G.
College: College of Science
Start Page: 42
End Page: 56