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

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 limited clustering, there were quite a few advances in tools, purposes, and our figuring out of the theoretical houses of constraints and limited clustering algorithms. Bringing those advancements jointly, Constrained Clustering: Advances in Algorithms, thought, and functions provides an intensive choice of the most recent concepts in clustering facts research tools 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 varieties of constraints for clustering, together with cluster dimension balancing, minimal cluster size,and cluster-level relational constraints.


It additionally describes diversifications of the normal clustering less than constraints challenge in addition to approximation algorithms with priceless functionality promises.


The booklet ends by means of utilizing clustering with constraints to relational facts, privacy-preserving info publishing, and video surveillance information. It discusses an interactive visible clustering procedure, a distance metric studying procedure, existential constraints, and instantly generated constraints.

With contributions from business researchers and best educational specialists who pioneered the sector, this quantity grants thorough assurance of the functions and boundaries of restricted clustering equipment in addition to introduces new different types of constraints and clustering algorithms.

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Extra resources for Constrained clustering: Advances in algorithms, theory, and applications

Example text

1. In some clustering problems the desired similarity metric may be so different from the default that traditional active learning would make many inefficient queries. This problem also arises when there are many different plausible clusterings. Although less automated, a human browsing the data would do less work by selecting the feedback data points themself. 2. The intuitive array of possible constraints are easier to apply than labels, especially when the final clusters are not known in advance.

Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society Series B (Methodological), 39(1):1–38, 1977. [9] Doug H. Fisher. Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2:139–172, 1987. [10] Doug H. Fisher. Iterative optimization and simplification of hierarchical clusterings. Journal of Artificial Intelligence Research, 4:147–180, 1996. [11] Jerome Friedman. Flexible metric nearest neighbor classication.

MacKay. Information Theory, Inference and Learning Algorithms. Cambridge University Press, 2003. [16] Andrew McCallum and Kamal Nigam. A comparison of event models for naive Bayes text classification. In Workshop on Learning for Text Categorization at the 15th Conference of the American Association for Artificial Intelligence, 1998. [17] Marina Meil˘ a and David Heckerman. An experimental comparison of several clustering and initialization methods. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI 98, pages 386– 395.

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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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