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Conference Paper/Proceeding/Abstract 673 views

Evaluating the Quality of Clustering Algorithms Using Cluster Path Lengths

F. Zaidi, D. Archambault, G. Melançon, Daniel Archambault Orcid Logo

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

Swansea University Author: Daniel Archambault Orcid Logo

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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
Online Access: http://www.scopus.com/inward/record.url?eid=2-s2.0-77954867692&partnerID=MN8TOARS
URI: https://cronfa.swan.ac.uk/Record/cronfa23049
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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