About Some Clustering Algorithms in Evidence Theory

Alexander Lepskiy ()
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Alexander Lepskiy: National Research University Higher School of Economics

A chapter in Data Analysis and Optimization, 2023, pp 179-190 from Springer

Abstract: Abstract The Dempster–Shafer theory of evidence considers data that have a frequency-set nature (the so-called body of evidence). In recent years, there has been interest in clustering such objects to approximate them with simpler bodies of evidence, to analyze the inconsistency of information, reducing the computational complexity of processing algorithms, revealing the structure of the set of focal elements, etc. The article discusses some existing algorithms for clustering evidence bodies and suggests some new algorithms and approaches in such clustering.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-031-31654-8_12

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DOI: 10.1007/978-3-031-31654-8_12

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