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The system provides an initial clustering of data. When the user selects a subset of the clusters for further examination, the system gathers their components and regroups them to form new clusters. Scatter/Gather aims at pursuing and finding structure in a small part of a corpus. This makes it an interesting complement to our approach: Scatter/Gather may provide an effective means for browsing and focusing on clusters of interest, and semi-supervised learning may be an effective means of improving the quality of those clusters.

Smyth, S. J. Camargo, and M. Ghil. Probabilistic clustering of extratropical cyclones using regression mixture models. Technical Report UCI-ICS 06-02, Bren School of Information and Computer Sciences, University of California, Irvine, January 2006. [12] D. Klein, S. D. Kamvar, and C. D. Manning. From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering. In Proceedings of the Nineteenth International Conference on Machine Learning, pages 307–313, 2002.

8] A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society Series B (Methodological), 39(1):1–38, 1977.

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