Toward Mixed-Initiative Email Clustering

Yifen Huang, Tom M. Mitchell

Organizing data into hierarchies is natural for humans. However, there is little work in machine learning that explores human-machine mixed-initiative approaches to organizing data into hierarchical clusters. In this paper we consider mixed-initiative clustering of a user's email, in which the machine produces (initial and retrained) hierarchical clusterings of email, and the user reviews and edits the initial hierarchical clustering, providing constraints on the re-trained clustering model. Key challenges include (a) determining types of feedback that users will find natural to provide, (b) developing hierarchical clustering and retraining algorithms capable of accepting these types of user feedback, (c) understanding how machine's clustering results and user feedback affect each other.

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