E-grāmata: Constrained Clustering: Advances in Algorithms, Theory, and Applications [Taylor & Francis e-book]

Clustering algorithms take data with any number of dimensions and group them into subsets so each member of a subset is near the other members in some sense. In 17 articles including an introduction, contributors describe this phenomenon, focusing on semi-supervise clustering with user feedback, Gaussian mixture models with equivalence constraints, pairwise constraints as priors in probabilistic clustering, clustering with constraints using a mean-field approximation perspectives, constraint-driven co-clustering of 0/1 data, supervised clustering for creating categorization segmentations, clustering with balancing constraints, assignment constraints that avoid empty clusters in k-means clustering, collective relational clustering, non-redundant data clustering, joint cluster analysis of attribute data and relationship data, correlation clustering, interactive visualization for relational data, distance metric learning, data publishing that preserves privacy, and learning with pairwise constraints for video object classification. Annotation ©2008 Book News, Inc., Portland, OR (booknews.com)

Since the initial work on constrained clustering, there have been numerous advances in methods, applications, and our understanding of the theoretical properties of constraints and constrained clustering algorithms. Bringing these developments together, Constrained Clustering: Advances in Algorithms, Theory, and Applications presents an extensive collection of the latest innovations in clustering data analysis methods that use background knowledge encoded as constraints.

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.