K-partitioning with imprecise probabilistic edges - Université de technologie de Compiègne Access content directly
Conference Papers Year : 2022

K-partitioning with imprecise probabilistic edges


Partitioning a set of elements into disjoint subsets is a common problem in unsupervised learning (clustering) as well as in networks (e.g., social, ecological) where one wants to find heterogeneous subgroups such that the elements within each subgroup are homogeneous. In this paper, we are concerned with the case where we imprecisely know the probability that two elements should belong to the same partition, and where we want to search the set of most probable partitions. We study the corresponding algorithmic problem on graphs, showing that it is difficult, and propose heuristic procedures that we test on data sets.
Fichier principal
Vignette du fichier
main.pdf (252.18 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03665950 , version 1 (12-05-2022)


  • HAL Id : hal-03665950 , version 1


Tom Davot, Sébastien Destercke, David Savourey. K-partitioning with imprecise probabilistic edges. 10th International Conference on Soft Methods in Probability and Statistics (SMPS 2022), May 2022, Valladolid, Spain. pp.87-95. ⟨hal-03665950⟩
74 View
116 Download


Gmail Mastodon Facebook X LinkedIn More