Localized alternative cluster ensembles for collaborative structuring

Abstract

Personal media collections are structured in very different ways by different users. Their support by standard clustering algorithms is not sufficient. First, users have their personal preferences which they hardly can express by a formal objective function. Instead, they might want to select among a set of proposed clusterings. Second, users most often do not want hand-made partial structures be overwritten by an automatic clustering. Third, given clusterings of others should not be ignored but used to enhance the own structure. In contrast to other cluster ensemble methods or distributed clustering, a global model (consensus) is not the aim. Hence, we investigate a new learning task, namely learning localized alternative cluster ensembles, where a set of given clusterings is taken into account and a set of proposed clusterings is delivered. This paper proposes an algorithm for solving the new task together with a method for evaluation. © Springer-Verlag Berlin Heidelberg 2006.