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Using soft consensus clustering for combining multiple clusterings of chemical structures

The consensus clustering has shown capability to improve the robustness, novelty and stability of individual clusterings in many areas including chemoinformatics. In this paper, graph-based consensus method (cluster-based similarity partitioning algorithm CSPA) and soft consensus clustering were exa...

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Main Authors: Saeed, Faisal, Salim, Naomie
Format: Article
Published: Penerbit UTM Press 2013
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/40798/
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spelling my.utm.407982017-11-01T04:17:12Z http://eprints.utm.my/id/eprint/40798/ Using soft consensus clustering for combining multiple clusterings of chemical structures Saeed, Faisal Salim, Naomie QA75 Electronic computers. Computer science The consensus clustering has shown capability to improve the robustness, novelty and stability of individual clusterings in many areas including chemoinformatics. In this paper, graph-based consensus method (cluster-based similarity partitioning algorithm CSPA) and soft consensus clustering were examined for combining multiple clusterings of chemical structures. The clustering is evaluated based on the ability to separate active from inactive molecules in each cluster. Experiments suggest that the effectiveness of soft consensus method can obtain better results than the hard consensus method (CSPA). Penerbit UTM Press 2013 Article PeerReviewed Saeed, Faisal and Salim, Naomie (2013) Using soft consensus clustering for combining multiple clusterings of chemical structures. Jurnal Teknologi, 63 (1). pp. 9-11. ISSN 2180-3722
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Saeed, Faisal
Salim, Naomie
Using soft consensus clustering for combining multiple clusterings of chemical structures
description The consensus clustering has shown capability to improve the robustness, novelty and stability of individual clusterings in many areas including chemoinformatics. In this paper, graph-based consensus method (cluster-based similarity partitioning algorithm CSPA) and soft consensus clustering were examined for combining multiple clusterings of chemical structures. The clustering is evaluated based on the ability to separate active from inactive molecules in each cluster. Experiments suggest that the effectiveness of soft consensus method can obtain better results than the hard consensus method (CSPA).
format Article
author Saeed, Faisal
Salim, Naomie
author_facet Saeed, Faisal
Salim, Naomie
author_sort Saeed, Faisal
title Using soft consensus clustering for combining multiple clusterings of chemical structures
title_short Using soft consensus clustering for combining multiple clusterings of chemical structures
title_full Using soft consensus clustering for combining multiple clusterings of chemical structures
title_fullStr Using soft consensus clustering for combining multiple clusterings of chemical structures
title_full_unstemmed Using soft consensus clustering for combining multiple clusterings of chemical structures
title_sort using soft consensus clustering for combining multiple clusterings of chemical structures
publisher Penerbit UTM Press
publishDate 2013
url http://eprints.utm.my/id/eprint/40798/
_version_ 1643650559890161664
score 13.209306

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