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Conference Paper/Proceeding/Abstract 1291 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
first_indexed 2015-09-12T02:08:27Z
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spelling 2015-09-11T09:18:02.4061490 v2 23049 2015-09-11 Evaluating the Quality of Clustering Algorithms Using Cluster Path Lengths 8fa6987716a22304ef04d3c3d50ef266 0000-0003-4978-8479 Daniel Archambault Daniel Archambault true false 2015-09-11 MACS Conference Paper/Proceeding/Abstract Advances in Data Mining. Applications and Theoretical Aspects 6171 LNAI 42 56 978-3-642-14399-1 978-3-642-14400-4 31 12 2010 2010-12-31 10.1007/978-3-642-14400-4_4 http://www.scopus.com/inward/record.url?eid=2-s2.0-77954867692&amp;partnerID=MN8TOARS @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 NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 2015-09-11T09:18:02.4061490 2015-09-11T09:14:17.8115093 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science F. Zaidi 1 D. Archambault 2 G. Melançon 3 Daniel Archambault 0000-0003-4978-8479 4
title Evaluating the Quality of Clustering Algorithms Using Cluster Path Lengths
spellingShingle Evaluating the Quality of Clustering Algorithms Using Cluster Path Lengths
Daniel Archambault
title_short Evaluating the Quality of Clustering Algorithms Using Cluster Path Lengths
title_full Evaluating the Quality of Clustering Algorithms Using Cluster Path Lengths
title_fullStr Evaluating the Quality of Clustering Algorithms Using Cluster Path Lengths
title_full_unstemmed Evaluating the Quality of Clustering Algorithms Using Cluster Path Lengths
title_sort Evaluating the Quality of Clustering Algorithms Using Cluster Path Lengths
author_id_str_mv 8fa6987716a22304ef04d3c3d50ef266
author_id_fullname_str_mv 8fa6987716a22304ef04d3c3d50ef266_***_Daniel Archambault
author Daniel Archambault
author2 F. Zaidi
D. Archambault
G. Melançon
Daniel Archambault
format Conference Paper/Proceeding/Abstract
container_title Advances in Data Mining. Applications and Theoretical Aspects
container_volume 6171 LNAI
container_start_page 42
publishDate 2010
institution Swansea University
isbn 978-3-642-14399-1
978-3-642-14400-4
doi_str_mv 10.1007/978-3-642-14400-4_4
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hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
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department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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published_date 2010-12-31T07:54:17Z
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