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A Clustering Model for Uncertain Preferences Based on Belief Functions

  • Yiru Zhang
  • Tassadit Bouadi
  • Arnaud Martin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11031)

Abstract

Community detection is a popular topic in network science field. In social network analysis, preference is often applied as an attribute for individuals’ representation. In some cases, uncertain and imprecise preferences may appear. Moreover, conflicting preferences can arise from multiple sources. From a model for imperfect preferences we proposed earlier, we study the clustering quality in case of perfect preferences as well as imperfect ones based on weak orders (orders that are complete, reflexive and transitive). The model for uncertain preferences is based on the theory of belief functions with an appropriate dissimilarity measure when performing the clustering steps. To evaluate the quality of clustering results, we used Adjusted Rand Index (ARI) and silhouette score on synthetic data as well as on Sushi preference data set collected from real world. The results show that our model has an equivalent quality with traditional preference representations for certain cases while it has better quality confronting imperfect cases.

Keywords

Clustering for orders Imperfect preference modeling Theory of belief functions 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Univ Rennes 1, CNRS, IRISARennesFrance

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