Constrained Clustering: Advances in Algorithms, Theory, and ApplicationsSugato Basu, Ian Davidson, Kiri Wagstaff CRC Press, 18. aug. 2008 - 472 sider This volume encompasses many new types of constraints and clustering methods as well as delivers thorough coverage of the capabilities and limitations of constrained clustering. With contributions from industrial researchers and leading academic experts who pioneered the field, it provides a well-balanced combination of theoretical advances, key algorithmic development, and novel applications. The book presents various types of constraints for clustering and describes useful variations of the standard problem of clustering under constraints. It also demonstrates the application of clustering with constraints to relational, bibliographic, and video data. |
Indhold
Noam Shental Aharon BarHillel Tomer Hertz and Daphna Weinshall | |
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Zhengdong Lu and Todd K Leen | |
Charu Aggarwal Stephen C Gates and Philip | |
Ayhan Demiriz Kristin P Bennett and Paul S Bradley | |
Indrajit Bhattacharya and Lise Getoor | |
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Constrained Clustering: Advances in Algorithms, Theory, and Applications Sugato Basu,Ian Davidson,Kiri Wagstaff Ingen forhåndsvisning - 2008 |
Almindelige termer og sætninger
approach approximation attributes C₁ cannot-link constraints chunklet classification cluster assignment cluster centers clustering algorithm clustering methods clustering problem co-clustering Conference on Machine constrained clustering constrained k-means data clustering data likelihood Data Mining data points data set database denote distance metric documents edges EM algorithm entity resolution equivalence constraints example Figure Gaussian mixture Gibbs sampling graph instances International Conference iteration k-means algorithm Knowledge Discovery labels learning algorithms loss function Machine Learning Markov network matrix metric learning minimize mixture model must-link constraints nodes number of clusters objective function optimization pairs pairwise constraints pairwise loss functions pairwise relations parameter partition performance pivot posterior probability prior probabilistic Proceedings proposed references samples Section semi-supervised clustering semi-supervised learning similarity solution supervised clustering supervised learning unlabelled data unsupervised vector weight x₁ πχ