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.
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Notes
- 1.
In decision making theory, different terms may refer to the same concepts. To avoid ambiguity, we unify the terminology concerning preferences. In this article, “agents” is used for individuals expressing their preferences, “alternatives” for items which are compared in preferences.
- 2.
As \(a_i\succeq a_j\) is equivalent to \(a_j\preceq a_i\), to avoid repetitive comparisons between two alternatives, we assume \(i>j\) in this article.
- 3.
In our work, we take \(p=0.5\).
- 4.
Without special remark, we use term “silhouette coefficient” for “average” value on set of samples by default.
- 5.
By saying neighbor size, we mean the number of samples in each cluster.
- 6.
As different K in EKNN-clus algorithm returns different clustering results, we compare clustering result who returns relatively high silhouette coefficient.
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Zhang, Y., Bouadi, T., Martin, A. (2018). A Clustering Model for Uncertain Preferences Based on Belief Functions. In: Ordonez, C., Bellatreche, L. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2018. Lecture Notes in Computer Science(), vol 11031. Springer, Cham. https://doi.org/10.1007/978-3-319-98539-8_9
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DOI: https://doi.org/10.1007/978-3-319-98539-8_9
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