Definition
Constrained clustering is a semisupervised approach to clustering data while incorporating domain knowledge in the form of constraints. The constraints are usually expressed as pairwise statements indicating that two items must, or cannot, be placed into the same cluster. Constrained clustering algorithms may enforce every constraint in the solution, or they may use the constraints as guidance rather than hard requirements.
Motivation and Background
Unsupervised learningoperates without any domain-specific guidance or preexisting knowledge. Supervised learning requires that all training examples be associated with labels. Yet it is often the case that existing knowledge for a problem domain fits neither of these extremes. Semisupervised learning methods fill this gap by making use of both labeled and unlabeled data. Constrained clustering, a form of semisupervised learning, was developed to extend clustering algorithms to incorporate existing domain knowledge, when...
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Recommended Reading
Bar-Hillel A, Hertz T, Shental N, Weinshall D (2005) Learning a Mahalanobis metric from equivalence constraints. J Mach Learn Res 6:937–965
Basu S, Bilenko M, Mooney RJ (2004) A probabilistic framework for semi-supervised clustering. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, Seattle, pp 59–68
Basu S, Davidson I, Wagstaff K (eds) (2008) Constrained clustering: advances in algorithms, theory, and applications. CRC Press, Boca Raton
Bilenko M, Basu S, Mooney RJ (2004) Integrating constraints and metric learning in semi-supervised clustering. In: Proceedings of the twenty-first international conference on machine learning, Banff, pp 11–18
Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc 39(1):1–38
Kamvar S, Klein D, Manning CD (2003) Spectral learning. In: Proceedings of the international joint conference on artificial intelligence, Acapulco, pp 561–566
Klein D, Kamvar SD, Manning CD (2002) From instance-level constraints to space-level constraints: making the most of prior knowledge in data clustering. In: Proceedings of the nineteenth international conference on machine learning, Sydney, pp 307–313
Lu Z, Leen T (2005) Semi-supervised learning with penalized probabilistic clustering. In: Advances in neural information processing systems, vol 17. MIT Press, Cambridge, MA, pp 849–856
MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth symposium on math, statistics, and probability, vol 1. University of California Press, California, pp 281–297
Shental N, Bar-Hillel A, Hertz T, Weinshall D (2004) Computing Gaussian mixture models with EM using equivalence constraints. In: Advances in neural information processing systems, vol 16. MIT Press, Cambridge, MA, pp 465–472
Wagstaff K, Cardie C (2000) Clustering with instance-level constraints. In: Proceedings of the seventeenth international conference on machine learning. Morgan Kaufmann, San Francisco, pp 1103–1110
Wagstaff K, Cardie C, Rogers S, Schroedl S (2001) Constrained k-means clustering with background knowledge. In: Proceedings of the eighteenth international conference on machine learning. Morgan Kaufmann, San Francisco, pp 577–584
Xing EP, Ng AY, Jordan MI, Russell S (2003) Distance metric learning, with application to clustering with side-information. In: Advances in neural information processing systems, vol 15. MIT Press, Cambridge, MA, pp 505–512
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media New York
About this entry
Cite this entry
Wagstaff, K.L. (2017). Constrained Clustering. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_163
Download citation
DOI: https://doi.org/10.1007/978-1-4899-7687-1_163
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4899-7685-7
Online ISBN: 978-1-4899-7687-1
eBook Packages: Computer ScienceReference Module Computer Science and Engineering