Abstract
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.
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Acknowledgements
This works partially supported by the project SIP 20196374 IPN and by Organizing Committee of RAAI Summer School. The author thanks all organizers of RAAI Summer School and editors of this book. Special thanks to doctors Gennady Osipov, Alexander Panov and Maria Koroleva.
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Batyrshin, I.Z. (2019). Data Science: Similarity, Dissimilarity and Correlation Functions. In: Osipov, G., Panov, A., Yakovlev, K. (eds) Artificial Intelligence. Lecture Notes in Computer Science(), vol 11866. Springer, Cham. https://doi.org/10.1007/978-3-030-33274-7_2
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