Text clustering is most commonly treated as a fully automated task without user feedback. However, a variety of researchers have explored mixed-initiative clustering methods which allow a user to interact with and advise the clustering algorithm. This mixed-initiative approach is especially attractive for text clustering tasks where the user is trying to organize a corpus of documents into clusters for some particular purpose (e.g., clustering their email into folders that reflect various activities in which they are involved). This paper introduces a new approach to mixed-initiative clustering that handles several natural types of user feedback. We first introduce a new probabilistic generative model for text clustering (the SpeClustering model) and show that it outperforms the commonly used mixture of multinomials clustering model, even when used in fully autonomous mode with no user input. We then describe how to incorporate four distinct types of user feedback into the clustering algorithm, and provide experimental evidence showing substantial improvements in text clustering when this user feedback is incorporated.
Huang and Mitchell REF propose a new generative clustering model and provide methods for the user to exert influence over the learned clusters.
Text clustering with extended user feedback
SIGIR '06
Computer Science