Semi-supervised Clustering by Seeding
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Traditional document clustering techniques group similar documents without any user interaction. Although such methods minimize user effort, the clusters they generate are often not in accord with their users' conception of the document collection. In this paper we describe a new framework and experiments with it exploring how clustering might be improved by including user supervision at the level of selecting features that are used to distinguish between documents. Our features are based on the words that appear in documents (see §4.1 for details.) We conjecture that clusters better matching user expectations can be generated with user input at the feature level. In order to verify our conjecture, we propose a novel iterative framework which involves users interactively selecting the features used to cluster documents. Unlike existing semi-supervised clustering, which asks users to label constraints between documents, this framework interactively asks users to label features. The proposed method ranks all features based on the recent clusters using cluster-based feature selection and presents a list of highly ranked features to users for labeling. The feature set for next clustering iteration includes both features accepted by users and other highly ranked features. The experimental results on several real datasets demonstrate that the feature set obtained using the new interactive framework can produce clusters that better match the user's expectations. Moreover, we quantify and evaluate the effect of reweighting previously accepted features and of user effort.