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

Because the preliminary paintings on limited clustering, there were various advances in equipment, functions, and our realizing of the theoretical houses of constraints and limited clustering algorithms. Bringing those advancements jointly, Constrained Clustering: Advances in Algorithms, thought, and purposes offers an in depth selection of the newest suggestions in clustering facts research tools that use history wisdom encoded as constraints.

Algorithms

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

thought

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

functions

The booklet ends through using clustering with constraints to relational facts, privacy-preserving facts publishing, and video surveillance info. It discusses an interactive visible clustering procedure, a distance metric studying process, existential constraints, and immediately generated constraints.

With contributions from business researchers and top educational specialists who pioneered the sphere, this quantity offers thorough insurance of the services and boundaries of restricted clustering equipment in addition to introduces new forms of constraints and clustering algorithms.

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

Example text

3 9 Theory The use of instance level constraints and clustering poses many computational challenges. It was recently proven that clustering with constraints raised an intractable feasibility problem [6, 8] for simply finding any clustering that satisfies all constraints via a reduction from graph coloring. It was later shown that attempts to side-step this feasibility problem by pruning constraint sets, or exactly or even approximately calculating k and trying to repair infeasible solutions, also lead to intractable problems [9].

Most importantly, semi-supervised clustering never expects a user to write a function that defines the clustering criterion. Instead, the user interacts with the clustering system, which attempts to learn a criterion that yields clusters the user is satisfied with. As such, one of the primary challenges of semisupervised clustering is finding ways to elicit and make use of user feedback during clustering. The remainder of this chapter describes one simple, illustrative way in which this may be accomplished.

In semi-supervised clustering, the human selects the data points, and puts on them a wide array of possible constraints instead of labels. These two key differences point toward some situations in which the semi-supervised approach is preferable. 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.

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