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In [[probability theory]] and [[information theory]], the '''variation of information''' or '''shared information distance''' is a measure of the distance between two clusterings ([[Partition of a set|partitions of elements]]). It is closely related to [[mutual information]]; indeed, it is a simple linear expression involving the mutual information. Unlike the mutual information, however, the variation of information is a true [[metric (mathematics)|metric]], in that it obeys the [[triangle inequality]]. Even more, it is a [[universal metric]], in that if any other distance measure two items close-by, then the variation of information will also judge them close.<ref>Alexander Kraskov, Harald Stögbauer, Ralph G. Andrzejak, and [[Peter Grassberger]], "Hierarchical Clustering Based on Mutual Information", (2003) ''[http://arxiv.org/abs/q-bio/0311039 ArXiv q-bio/0311039]''</ref>
 
==Background==
{{Empty section|date=July 2010}}
 
==Definition==
Suppose we have two clusterings (a division of a [[Set (mathematics)|set]] into several [[subset]]s) <math>X</math> and <math>Y</math> where <math>X = \{X_{1}, X_{2}, ..,, X_{k}\}</math>, <math>p_{i} = |X_{i}| / n</math>, <math>n = \Sigma_{k} |X_{i}|</math>. Then the variation of information between two clusterings is:
 
:<math>VI(X; Y ) = H(X) + H(Y) - 2I(X, Y)</math>
 
where <math>H(X)</math> is [[entropy (information theory)|entropy]] of <math>X</math> and <math>I(X, Y)</math> is [[mutual information]] between <math>X</math> and <math>Y</math>.
 
This is completely equivalent to the [[Mutual information#Metric|shared information distance]].
 
==References==
<references/>
 
== Further reading ==
* {{cite journal|last=Arabie|first=P.|coauthors=Boorman, S. A.|title=Multidimensional scaling of measures of distance between partitions|journal=Journal of Mathematical Psychology|year=1973|volume=10|pages=148–203}}
* {{Cite journal|last=Meila|first=Marina | title=Comparing Clusterings by the Variation of Information |journal=Learning Theory and Kernel Machines|year=2003|pages=173–187|doi=10.1007/978-3-540-45167-9_14}}
* {{cite doi|10.1016/j.jmva.2006.11.013}}
* {{Cite web
  | last = Kingsford
  | first = Carl
  | title = Information Theory Notes
  | year = 2009
  | url = http://www.cs.umd.edu/class/spring2009/cmsc858l/InfoTheoryHints.pdf
  | format = PDF
  | accessdate = 22 September 2009}}
{{Use dmy dates|date=September 2010}}
 
== External links ==
* [https://github.com/bjoern-andres/partition-comparison C++ implementation with MATLAB mex files]
 
{{DEFAULTSORT:Variation Of Information}}
[[Category:Entropy and information]]

Revision as of 08:40, 3 February 2014

In probability theory and information theory, the variation of information or shared information distance is a measure of the distance between two clusterings (partitions of elements). It is closely related to mutual information; indeed, it is a simple linear expression involving the mutual information. Unlike the mutual information, however, the variation of information is a true metric, in that it obeys the triangle inequality. Even more, it is a universal metric, in that if any other distance measure two items close-by, then the variation of information will also judge them close.[1]

Background

Template:Empty section

Definition

Suppose we have two clusterings (a division of a set into several subsets) and where , , . Then the variation of information between two clusterings is:

where is entropy of and is mutual information between and .

This is completely equivalent to the shared information distance.

References

  1. Alexander Kraskov, Harald Stögbauer, Ralph G. Andrzejak, and Peter Grassberger, "Hierarchical Clustering Based on Mutual Information", (2003) ArXiv q-bio/0311039

Further reading

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