Proceedings ArticleDOI
Information theoretic measures for clusterings comparison: is a correction for chance necessary?
Nguyen Xuan Vinh,Julien Epps,James Bailey +2 more
- pp 1073-1080
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TLDR
This paper derives the analytical formula for the expected mutual information value between a pair of clusterings, and proposes the adjusted version for several popular information theoretic based measures.Abstract:
Information theoretic based measures form a fundamental class of similarity measures for comparing clusterings, beside the class of pair-counting based and set-matching based measures. In this paper, we discuss the necessity of correction for chance for information theoretic based measures for clusterings comparison. We observe that the baseline for such measures, i.e. average value between random partitions of a data set, does not take on a constant value, and tends to have larger variation when the ratio between the number of data points and the number of clusters is small. This effect is similar in some other non-information theoretic based measures such as the well-known Rand Index. Assuming a hypergeometric model of randomness, we derive the analytical formula for the expected mutual information value between a pair of clusterings, and then propose the adjusted version for several popular information theoretic based measures. Some examples are given to demonstrate the need and usefulness of the adjusted measures.read more
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Journal ArticleDOI
Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance
TL;DR: An organized study of information theoretic measures for clustering comparison, including several existing popular measures in the literature, as well as some newly proposed ones, and advocates the normalized information distance (NID) as a general measure of choice.
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References
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Book
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TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.
Journal ArticleDOI
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TL;DR: This article proposes several criteria which isolate specific aspects of the performance of a method, such as its retrieval of inherent structure, its sensitivity to resampling and the stability of its results in the light of new data.
Journal ArticleDOI
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
Alexander Strehl,Joydeep Ghosh +1 more
TL;DR: This paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that determined these partitionings and proposes three effective and efficient techniques for obtaining high-quality combiners (consensus functions).
Journal ArticleDOI
Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data
TL;DR: A new methodology of class discovery and clustering validation tailored to the task of analyzing gene expression data is presented, and in conjunction with resampling techniques, it provides for a method to represent the consensus across multiple runs of a clustering algorithm and to assess the stability of the discovered clusters.
Journal ArticleDOI
Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance
TL;DR: An organized study of information theoretic measures for clustering comparison, including several existing popular measures in the literature, as well as some newly proposed ones, and advocates the normalized information distance (NID) as a general measure of choice.