  In the collaborative clustering framework, the hope is that by combining
several clustering solutions, each one with its own bias and imperfections, one
will get a better overall solution. The goal is that each local computation,
quite possibly applied to distinct data sets, benefits from the work done by
the other collaborators. This article is dedicated to collaborative clustering
based on the Learning Using Privileged Information paradigm. Local algorithms
weight incoming information at the level of each observation, depending on the
confidence level of the classification of that observation. A comparison
between our algorithm and state of the art implementations shows improvement of
the collaboration process using the proposed approach.
