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Because the preliminary paintings on restricted clustering, there were a number of advances in tools, functions, and our knowing of the theoretical homes of constraints and limited clustering algorithms. Bringing those advancements jointly, Constrained Clustering: Advances in Algorithms, conception, and Applications offers an in depth choice of the newest ideas in clustering info research equipment that use heritage 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 different types of constraints for clustering, together with cluster measurement balancing, minimal cluster size,and cluster-level relational constraints.
It additionally describes diversifications of the normal clustering below constraints challenge in addition to approximation algorithms with precious functionality promises.
The ebook ends by means of using clustering with constraints to relational facts, privacy-preserving facts publishing, and video surveillance info. It discusses an interactive visible clustering strategy, a distance metric studying process, existential constraints, and immediately generated constraints.
With contributions from commercial researchers and major educational specialists who pioneered the sector, 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 resources for Constrained Clustering: Advances in Algorithms, Theory, and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
Acknowledgments We thank Douglas Fisher for his thoughtful and thought-provoking comments, which contributed to the content of this chapter. We also thank the National Science Foundation for the support of our own work on constrained clustering via grants IIS-0325329 and IIS-0801528. The ﬁrst author would additionally like to thank Google, IBM, and DARPA for supporting some of his work through their research grant, fellowship program, and contract #NBCHD030010 (Order-T310), respectively. 5 Notation and Symbols The following table summarizes the notation that we shall use throughout this book.
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.  I. Davidson and S. S. Ravi. Generating easy sets of constraints for clustering. In Proceedings of the 2006 AAAI Conference, Boston, MA, 2006.  I. Davidson and S. S. Ravi. The complexity of non-hierarchical clustering with instance and cluster level constraints. Data Mining and Knowledge Discovery, 14:25–61, 2007.
2. The intuitive array of possible constraints are easier to apply than labels, especially when the ﬁnal clusters are not known in advance. 3. The very act of human browsing can lead to the discovery of what clusters are desired. Semi-supervised learning can thus be seen as a method of data exploration and pattern discovery, eﬃciently aided by cluster-based summarization. 2 Demiriz et al.  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.