Comparing clusterings---an information based distance
TLDR
This paper proposes an information theoretic criterion for comparing two partitions, or clusterings, of the same data set, called variation of information (VI), and presents it from an axiomatic point of view, showing that it is the only ''sensible'' criterion for compare partitions that is both aligned to the lattice and convexely additive.About:
This article is published in Journal of Multivariate Analysis.The article was published on 2007-05-01 and is currently open access. It has received 1527 citations till now. The article focuses on the topics: Variation of information & Cluster analysis.read more
Citations
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Journal ArticleDOI
Community detection in graphs
TL;DR: A thorough exposition of community structure, or clustering, is attempted, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists.
Journal ArticleDOI
Community detection in graphs
TL;DR: A thorough exposition of the main elements of the clustering problem can be found in this paper, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.
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Community detection algorithms: a comparative analysis.
TL;DR: Three recent algorithms introduced by Rosvall and Bergstrom and Ronhovde and Nussinov have an excellent performance, with the additional advantage of low computational complexity, which enables one to analyze large systems.
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Detecting the overlapping and hierarchical community structure in complex networks
TL;DR: The first algorithm that finds both overlapping communities and the hierarchical structure is presented, based on the local optimization of a fitness function, enabling different hierarchical levels of organization to be investigated.
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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.
References
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Book
Elements of information theory
Thomas M. Cover,Joy A. Thomas +1 more
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
Least squares quantization in PCM
TL;DR: In this article, the authors derived necessary conditions for any finite number of quanta and associated quantization intervals of an optimum finite quantization scheme to achieve minimum average quantization noise power.
Least Squares Quantization in PCM
TL;DR: The corresponding result for any finite number of quanta is derived; that is, necessary conditions are found that the quanta and associated quantization intervals of an optimum finite quantization scheme must satisfy.