Clustering Objects from Multiple Collections

Abstract

Clustering methods cluster objects on the basis of a similarity measure between the objects. In clustering tasks where the objects come from more than one collection often part of the similarity results from features that are related to the collections rather than features that are relevant for the clustering task. For example, when clustering pages from various web sites by topic, pages from the same web site often contain similar terms. The collection-related part of the similarity hinders clustering as it causes the creation of clusters that correspond to collections instead of topics. In this paper we present two methods to restrict clustering to the part of the similarity that is not associated with membership of a collection. Both methods can be used on top of standard clustering methods. Experiments on data sets with objects from multiple collections show that our methods result in better clusters than methods that do not take collection information into account.

This research is part of the project ‘Adaptive generation of workflow models for human-computer interaction’ (project MMI06101) funded by SenterNovem.