Semi-supervised Collaborative Clustering with Partial Background Knowledge
Germain Forestier, Cédric Wemmert, Pierre Gançarski · 2008
In this paper we present a new algorithm for semisupervised clustering. We assume to have a small set of labeled samples and we use it in a clustering algorithm to discover relevant patterns. We study how our algorithm works against two other semisupervised algorithms when the data are multimodal. Then, we study the case where the user is able to produce few samples for some classes but not for each class of the dataset. Indeed, in complex problems, the user is not always able to produce samples for each class present in the dataset. The challenging task is consequently to use the set of labeled samples to discover other members of these classes, but also to keep a degree of freedom to discover unknown clusters, for which samples are not available. We address this problem through a series of experimentations on synthetic datasets, to show the relevance of the proposed method.