Understanding information theoretic measures for comparing clusterings
نویسندگان
چکیده
منابع مشابه
Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance
Information theoretic measures form a fundamental class of measures for comparing clusterings, and have recently received increasing interest. Nevertheless, a number of questions concerning their properties and inter-relationships remain unresolved. In this paper, we perform an organized study of information theoretic measures for clustering comparison, including several existing popular measur...
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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 C to clustering C′. The basic properties of VI are presented and discussed. We focus on two kinds of properties: (1) those that help one build...
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In the past three decades, many theoretical measures of complexity have been proposed to help understand complex systems. In this work, for the first time, we place these measures on a level playing field, to explore the qualitative similarities and differences between them, and their shortcomings. Specifically, using the Boltzmann machine architecture (a fully connected recurrent neural networ...
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ژورنال
عنوان ژورنال: Behaviormetrika
سال: 2018
ISSN: 0385-7417,1349-6964
DOI: 10.1007/s41237-018-0075-7