Clustering approaches for tensor data

Rafika Boutalbi
LIS, Aix-Marseille Université

Date(s) : 25/11/2022   iCal
14 h 30 min - 15 h 30 min

Dealing with relational learning generally relies on tools modeling relational data. An undirected graph can represent these data with vertices depicting entities and edges describing the relationships between the entities. These relationships can be well represented by multiple undirected graphs over the same set of vertices, with edges arising from different graphs catching heterogeneous relations. The vertices of those networks are often structured in unknown clusters with varying properties of connectivity. These multiple graphs can be structured as a three-way tensor, where each slice of tensor depicts a graph that is represented by a count data matrix. To extract relevant clusters, I will present a variety of model-based clustering capable of dealing with multiple graphs.

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