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Editorial Reviews
Review
… this book shows how constrained clustering can be used to tackle large problems involving textual, relational, and even video data. After reading this book, you will have the tools to be a better analyst [and] to gain more insight from your data, whether it be textual, audio, video, relational, genomic, or anything else.
—Dr. Peter Norvig, Director of Research, Google, Inc., Mountain View, California, USA
Product Description
Algorithms
The first five chapters of this volume investigate advances in the use of instance-level, pairwise constraints for partitional and hierarchical clustering. The book then explores other types of constraints for clustering, including cluster size balancing, minimum cluster size,and cluster-level relational constraints.
Theory
It also describes variations of the traditional clustering under constraints problem as well as approximation algorithms with helpful performance guarantees.
Applications
The book ends by applying clustering with constraints to relational data, privacy-preserving data publishing, and video surveillance data. It discusses an interactive visual clustering approach, a distance metric learning approach, existential constraints, and automatically generated constraints.
With contributions from industrial researchers and leading academic experts who pioneered the field, this volume delivers thorough coverage of the capabilities and limitations of constrained clustering methods as well as introduces new types of constraints and clustering algorithms.
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probabilistic clustering, cluster size constraints, amino acid, support vector learning, pairwise loss functions, interactive visual clustering, pivot movement graph, joint cluster analysis, conditional information bottleneck, dissimilar example pairs, pivot movement phase, unstable pivot objects, potential bridge node, video object classification, deadlock resolution phase, clustering baseline, surplus cluster, base clustering method, constrained clustering, clustering with constraints, pairwise constraints, correct decision boundaries, cluster assignment step, unstable pivots, kernel logistic regression Key Phrases - Capitalized Phrases (CAPs): (learn more)
International Conference, Machine Learning, Data Mining, Non-Redundant Data Clustering, Neural Information Processing Systems, Mean-Field Approximation Perspective, Morgan Kaufmann, Gaussian Mixture Models, Privacy-Preserving Data Publishing, Information Retrieval, Nodes Moved, New York, Artificial Intelligence, Cannot-be-Linked Example Pairs, Technical Report, Lecture Notes, Neural Networks, Computer Science, Overlapping Circles, Management of Data, Third Category, Data Engineering, Second Category, Computer Vision, John Wiley Browse Sample Pages:
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