Quentin HAENN
  • PhD student
  • Data Engineering

Research Activity

Clustering under similarity constraints

My thesis is about an in-depth study of a specific approach of clustering under similarity constraints. Indeed, we plan to study, test and elaborate algorithms and implementations allowing to perform the clustering operation under an intra cluster similarity constraint. In this sense, we address the diameter constraint and radius constraint approaches. 

Moreover, we also aim to explore the main ways to implement those algorithms. Several approaches are already being considered, particularly graph theory with approaches using minimal dominating set.

The technical objective is to develop and release a complete clustering library, containing several implementations we develop, an in-depth study of the algorithms complexity and a study of the mathematical guarantees offered by our implementations.