Download e-book for kindle: Constrained clustering: Advances in algorithms, theory, and by Sugato Basu, Ian Davidson, Visit Amazon's Kiri Wagstaff

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By Sugato Basu, Ian Davidson, Visit Amazon's Kiri Wagstaff Page, search results, Learn about Author Central, Kiri Wagstaff,

ISBN-10: 1584889969

ISBN-13: 9781584889960

ISBN-10: 1584889977

ISBN-13: 9781584889977

Because the preliminary paintings on restricted clustering, there were a variety of advances in equipment, functions, and our knowing of the theoretical houses of constraints and restricted clustering algorithms. Bringing those advancements jointly, Constrained Clustering: Advances in Algorithms, concept, and purposes provides an intensive choice of the newest strategies in clustering information research equipment that use historical past wisdom encoded as constraints.


The first 5 chapters of this quantity examine advances within the use of instance-level, pairwise constraints for partitional and hierarchical clustering. The e-book then explores different sorts of constraints for clustering, together with cluster measurement balancing, minimal cluster size,and cluster-level relational constraints.


It additionally describes adaptations of the normal clustering below constraints challenge in addition to approximation algorithms with beneficial functionality promises.


The ebook ends by way of employing clustering with constraints to relational info, privacy-preserving info publishing, and video surveillance info. It discusses an interactive visible clustering strategy, a distance metric studying process, existential constraints, and instantly generated constraints.

With contributions from business researchers and major educational specialists who pioneered the sphere, this quantity offers thorough assurance of the services and obstacles of restricted clustering equipment in addition to introduces new different types of constraints and clustering algorithms.

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Additional info for Constrained clustering: Advances in algorithms, theory, and applications

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Integrating constraints and metric learning in semi-supervised clustering. In Proceedings of the TwentyFirst International Conference on Machine Learning, pages 11–18, 2004. [5] P. Cheeseman and J. Stutz. Bayesian classification (autoclass): Theory and results. In Advances in Knowledge Discovery and Data Mining, pages 153–180. Morgan Kaufmann, 1996. [6] I. Davidson and S. S. Ravi. Clustering with constraints: Feasibility issues and the k-means algorithm. In Proceedings of the 2005 SIAM International Conference on Data Mining, pages 138–149, Newport Beach, CA, 2005.

2 Demiriz et al. [7] independently introduced a semi-supervised clustering model similar to the one we describe here. The main distinction between our work and theirs is our use of iterative feedback to acquire labelings; Demiriz et al. assume that all available labels are given a priori. 20 Constrained Clustering: Advances in Algorithms, Theory, and Applications However, the distinction with active learning is subjective. 1, our system could easily be viewed as a practical application of learning by counterexamples [1] – one of the earliest and most powerful forms of active learning studied in the theory community.

2 Initial Work: Instance-Level Constraints A clustering problem can be thought of as a scenario in which a user wishes to obtain a partition ΠX of a data set X, containing n items, into k clusters πi = ∅). A constrained clustering problem is (ΠX = π1 ∪ π2 . . ∪ πk , one in which the user has some pre-existing knowledge about their desired ΠX . The first introduction of constrained clustering to the machine learning and data mining communities [16, 17] focused on the use of instance-level constraints.

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Constrained clustering: Advances in algorithms, theory, and applications by Sugato Basu, Ian Davidson, Visit Amazon's Kiri Wagstaff Page, search results, Learn about Author Central, Kiri Wagstaff,

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