Abstract
Two algorithms for the body of evidence clustering are developed and studied in this paper. The first algorithm is based on the use of the distribution density function of conflicting focal elements of the body of evidence. The second algorithm is similar to the k-means algorithm, but it uses the external conflict measure instead of the metric. It is shown that cluster decomposition can be used to evaluate the internal conflict of the body of evidence.
The financial support from the Government of the Russian Federation within the framework of the implementation of the 5–100 Programme Roadmap of the National Research University Higher School of Economics is acknowledged.
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Lepskiy, A. (2022). Cluster Decomposition of the Body of Evidence. In: Le Hégarat-Mascle, S., Bloch, I., Aldea, E. (eds) Belief Functions: Theory and Applications. BELIEF 2022. Lecture Notes in Computer Science(), vol 13506. Springer, Cham. https://doi.org/10.1007/978-3-031-17801-6_16
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