4 Result(s)
within
Chapter
Kiri L. Wagstaff
English
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Chapter and Conference Paper
Value, Cost, and Sharing: Open Issues in Constrained Clustering
Clustering 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 method...
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Chapter and Conference Paper
Active Learning with Irrelevant Examples
Active learning algorithms attempt to accelerate the learning process by requesting labels for the most informative items first. In real-world problems, however, there may exist unlabeled items that are irrele...
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Chapter and Conference Paper
Measuring Constraint-Set Utility for Partitional Clustering Algorithms
Clustering with constraints is an active area of machine learning and data mining research. Previous empirical work has convincingly shown that adding constraints to clustering improves performance, with respe...
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Chapter and Conference Paper
Active Constrained Clustering by Examining Spectral Eigenvectors
This work focuses on the active selection of pairwise constraints for spectral clustering. We develop and analyze a technique for Active Constrained Clustering by Examining Spectral eigenvectorS (ACCESS) deriv...
