Abstract
The notions of similarity and dissimilarity are widely used in many fields of Artificial Intelligence. They have many different and often partial definitions or properties, usually restricted to one field of application and thus incompatible with other uses. This paper contributes to the design and understanding of similarity and dissimilarity measures for Artificial Intelligence. A formal dual definition for each concept is proposed, joined with a set of fundamental properties. The behavior of the properties under several transformations is studied and revealed as an important matter to bear in mind. We also develop several practical examples that work out the proposed approach.
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Belanche, L., Orozco, J. (2011). Things to Know about a (dis)similarity Measure. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2011. Lecture Notes in Computer Science(), vol 6881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23851-2_11
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DOI: https://doi.org/10.1007/978-3-642-23851-2_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23850-5
Online ISBN: 978-3-642-23851-2
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