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Many studies show that constraint-based mining is highly desirable since it often leads to effective and fruitful data mining by capturing application semantics ...
In this paper, we introduce the constrained clustering problem and show that traditional clustering algorithms (e.g., k-means) cannot handle it. A scalable ...
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A scalable constraint-clustering algorithm is developed in this study which starts by finding an initial solution that satisfies user-specified constraints.
Dec 5, 2024 · Constrained clustering — finding clusters that satisfy user-specified constraints — is highly desirable in many applications.
A scalable constraint-clustering algorithm is developed in this study which starts by finding an initial solution that satisfies user-specified constraints and ...
A scalable constraint-clustering algorithm is developed in this study which starts by finding an initial solution that satisfies user-specified constraints ...
A scalable constraint-clustering algorithm is developed in this study which starts by finding an initial solution that satisfies user-specified constraints and ...
The idea is to increase the size of the cluster with data objects as long as the density in the “neighborhood” exceeds some threshold, i.e., for each data point ...
May 11, 2022 · Constrained clustering is an approach to clustering the data while it incorporates the domain knowledge in form of constraints.
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