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,

Because the preliminary paintings on restricted clustering, there were quite a few advances in tools, purposes, and our realizing of the theoretical homes of constraints and limited clustering algorithms. Bringing those advancements jointly, Constrained Clustering: Advances in Algorithms, conception, and Applications provides an intensive number of the newest strategies in clustering info research equipment that use history 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 publication then explores different different types of constraints for clustering, together with cluster dimension balancing, minimal cluster size,and cluster-level relational constraints.


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


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

With contributions from commercial researchers and best 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 varieties of constraints and clustering algorithms.

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Additional resources for Constrained Clustering: Advances in Algorithms, Theory, and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Example text

It can be broadly defined as the process of dividing a set of objects into clusters, each of which represents a meaningful sub-population. The objects may be database records, nodes in a graph, words, images, or any collection in which individuals are described by a set of features or distinguishing relationships. , Gaussian distribution) and the observed data distribution. These methods have led to new insights into large data sets from a host of scientific fields, including astronomy [5], bioinformatics [13], meteorology [11], and others.

After your critique, re-cluster the documents, allowing the clustering algorithm to modify the the distance metric parameters to try to find a new clustering that satisfies the constraints you provided in the critique. 4. Repeat this until you are happy with the clustering. This solution is distinct from both traditional supervised and unsupervised learning. Unsupervised clustering takes an unlabeled collection of data and, without intervention or additional knowledge, partitions it into sets of examples such that examples within clusters are more “similar” than examples between clusters.

For the supervised learner, we plot both cluster purity and classification accuracy (generalization). After only a few constraints have been added, cluster purity increases sharply over that of unsupervised clustering. It is not clear, however, how to fairly compare the performance of semi-supervised clustering with that of fully supervised clustering: constraints do not exactly correspond to labeled examples, and it is uncertain what constitutes a proper test set. In supervised learning, documents used for training are traditionally excluded from the test set, since their labels are already known.

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