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Value, Cost, and Sharing: Open Issues in Constrained ClusteringClustering is an important tool for data mining, since it can identify major patterns or trends without any supervision (labeled data). Over the past five years, semi-supervised (constrained) clustering methods have become very popular. These methods began with incorporating pairwise constraints and have developed into more general methods that can learn appropriate distance metrics. However, several important open questions have arisen about which constraints are most useful, how they can be actively acquired, and when and how they should be propagated to neighboring points. This position paper describes these open questions and suggests future directions for constrained clustering research.
Document ID
20070032878
Document Type
Preprint (Draft being sent to journal)
External Source(s)
Authors
Wagstaff, Kiri L. (Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
August 23, 2013
Publication Date
September 18, 2006
Subject Category
Social and Information Sciences (General)
Meeting Information
5th International Workshop on Knowledge Discovery in Inducative Databases (KDID''06)(Berlin)
Funding Number(s)
CONTRACT_GRANT: NSF ITR-03-25329
Distribution Limits
Public
Copyright
Other
Keywords
constraints
clustering