Comparing Clusterings by the Variation of Information
- Marina Meilă
- … show all 1 hide
Abstract
This paper proposes an information theoretic criterion for comparing two partitions, or clusterings, of the same data set. The criterion, called variation of information (VI), measures the amount of information lost and gained in changing from clustering



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References (12)
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About this Chapter
- Title
- Comparing Clusterings by the Variation of Information
- Book Title
- Learning Theory and Kernel Machines
- Book Subtitle
- 16th Annual Conference on Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003. Proceedings
- Pages
- pp 173-187
- Copyright
- 2003
- DOI
- 10.1007/978-3-540-45167-9_14
- Print ISBN
- 978-3-540-40720-1
- Online ISBN
- 978-3-540-45167-9
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- 2777
- Series ISSN
- 0302-9743
- Publisher
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
- Additional Links
- Topics
- Keywords
-
- Clustering
- Comparing partitions
- Measures of agreement
- Information theory
- Mutual information
- Industry Sectors
- eBook Packages
- Editors
-
-
Bernhard Schölkopf
(6)
-
Manfred K. Warmuth
(7)
-
Bernhard Schölkopf
- Editor Affiliations
-
- 6. MPI for Biological Cybernetics
- 7. University of California
- Authors
-
-
Marina Meilă
(8)
-
Marina Meilă
- Author Affiliations
-
- 8. University of Washington, Box 354322, Seattle, WA, 98195-4322, USA
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