Data science: Similarity, dissimilarity and correlation functions

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Resumen

The lecture presents a new, non-statistical approach to the analysis and construction of similarity, dissimilarity and correlation measures. The measures are considered as functions defined on an underlying set and satisfying the given properties. Different functional structures, relationships between them and methods of their construction are discussed. Particular attention is paid to functions defined on sets with an involution operation, where the class of (strong) correlation functions is introduced. The general methods constructing new correlation functions from similarity and dissimilarity functions are considered. It is shown that the classical correlation and association coefficients (Pearson’s, Spearman’s, Kendall’s, Yule’s Q, Hamann) can be obtained as particular cases.

Idioma originalInglés
Título de la publicación alojadaArtificial Intelligence - 5th RAAI Summer School, 2019, Tutorial Lectures
EditoresGennady S. Osipov, Aleksandr I. Panov, Konstantin S. Yakovlev
EditorialSpringer
Páginas13-28
Número de páginas16
ISBN (versión impresa)9783030332730
DOI
EstadoPublicada - 2019
Evento5th RAAI Summer School on Artificial Intelligence, 2019 - Dolgoprudny, Federación de Rusia
Duración: 4 jul. 20197 jul. 2019

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen11866 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia5th RAAI Summer School on Artificial Intelligence, 2019
País/TerritorioFederación de Rusia
CiudadDolgoprudny
Período4/07/197/07/19

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