Constrained clustering

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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. Both a must-link and a cannot-link constraint define a relationship between two data instances. 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. These sets of constraints acts as a guide for which a constrained clustering algorithm will attempt to find clusters in a data set which satisfy the specified must-link and cannot-link constraints. 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.

Examples of constrained clustering algorithms include:

  • COP K-means [1]
  • PCKmeans
  • CMWK-Means [2]

References[edit]

  1. ^ 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. 
  2. ^ 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. 

Further reading[edit]