Integrating pairwise constraints into clustering algorithms: optimization-based approaches

Jacques-Henri Sublemontier (1) , Lionel Martin (2) , Guillaume Cleuziou (2) , Matthieu Exbrayat (2)

1 CA
2 LIFO - Laboratoire d'Informatique Fondamentale d'Orléans

In this paper we introduce new models for semi-supervised clustering problem; in particular we address this problem from the representation space point of view. Given a dataset enhanced with constraints (typically must-link and cannot-link constraints) and any clustering algorithm, the proposed approach aims at learning a projection space for the dataset that satisfies not only the constraints but also the required objective of the clustering algorithm on unenhanced data. We propose a boosting framework to weight the constraints and infers successive projection spaces in such a way that algorithm performance is improved. We experiment this approach on standard UCI datasets and show the effectiveness of our algorithm.

Keywords: machine learning; data mining; semi-supervised clustering

Domains: Machine Learning [cs.LG]

Jacques-Henri Sublemontier, Lionel Martin, Guillaume Cleuziou, Matthieu Exbrayat. Integrating pairwise constraints into clustering algorithms: optimization-based approaches. OEDM 2011, Optimization based approaches for Emerging Data Mining problems, Dec 2011, Vancouver, Canada. pp._. ⟨hal-00632952⟩