Constrained clustering
In today's world, Constrained clustering is a highly relevant issue that impacts society in different aspects. For decades, Constrained clustering has been the object of study and interest by experts, researchers and professionals in different areas. Its influence ranges from economics to politics, including culture and the environment. In this article, we will explore in detail the different aspects related to Constrained clustering, analyzing its importance, its implications and the possible future scenarios that its development may entail. From its origins to the present, Constrained clustering has given rise to endless debates and reflections that have contributed to enriching knowledge about this phenomenon.
In computer science, constrained clustering is a class of semi-supervised learning algorithms. Typically, constrained clustering incorporates either a set of must-link constraints, cannot-link constraints, or both, with a data clustering algorithm.[1] A cluster in which the members conform to all must-link and cannot-link constraints is called a chunklet.
Types of constraints
Both a must-link and a cannot-link constraint define a relationship between two data instances. Together, the sets of these constraints act as a guide for which a constrained clustering algorithm will attempt to find chunklets (clusters in the dataset which satisfy the specified constraints).
- A must-link constraint is used to specify that the two instances in the must-link relation should be associated with the same cluster.
- A cannot-link constraint is used to specify that the two instances in the cannot-link relation should not be associated with the same cluster.
Some constrained clustering algorithms will abort if no such clustering exists which satisfies the specified constraints. Others will try to minimize the amount of constraint violation should it be impossible to find a clustering which satisfies the constraints. Constraints could also be used to guide the selection of a clustering model among several possible solutions.[2]
Examples
Examples of constrained clustering algorithms include:
- COP K-means [1]
- PCKmeans (Pairwise Constrained K-means) [3]
- CMWK-Means (Constrained Minkowski Weighted K-Means) [4]
References
- ^ a b Wagstaff, K.; Cardie, C.; Rogers, S.; Schrödl, S. (2001). "Constrained K-means Clustering with Background Knowledge". Proceedings of the Eighteenth International Conference on Machine Learning. pp. 577–584.
- ^ Pourrajabi, M.; Moulavi, D.; Campello, R. J. G. B.; Zimek, A.; Sander, J.; Goebel, R. (2014). "Model Selection for Semi-Supervised Clustering". Proceedings of the 17th International Conference on Extending Database Technology (EDBT). pp. 331–342. doi:10.5441/002/edbt.2014.31.
- ^ Basu, Sugato; Banerjee, Arindam; Mooney, Raymond J. (April 2004). Active Semi-Supervision for Pairwise Constrained Clustering (PDF). Proceedings of the 2004 SIAM International Conference on Data Mining. pp. 333–344.
- ^ de Amorim, R. C. (2012). "Constrained Clustering with Minkowski Weighted K-Means". Proceedings of the 13th IEEE International Symposium on Computational Intelligence and Informatics. pp. 13–17. doi:10.1109/CINTI.2012.6496753.
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