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Constrained Clustering

Формат:      Страниц 88
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Lambert M. S.    
Книга по Требованию    
1315 руб
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In computer science, constrained clustering is a class of semi-supervised learning algorithms. 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. A must-link constraint is used to specify that the two instances in the must-link relation should be associated with the same cluster. Both a must-link and a cannot-link constraint define a relationship between two data instances. 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. Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Some constrained clustering algorithms will abort if no such clustering exists which satisfies the specified constraints. Typically, constrained clustering incorporates either a set of must-link constraints, cannot-link constraints, or both, with a Data clustering algorithm. Others will try to minimize the amount of constraint violation should it be impossible to find a clustering which satisfies the constraints.
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