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• W2938077962 abstract "Clustering is one of the fundamental tasks in data analytics and machine learning. In many situations, different clusterings of the same data set become relevant. For example, different algorithms for the same clustering task may return dramatically different solutions. We are interested in applications in which one clustering has to be transformed into another; e.g., when a gradual transition from an old solution to a new one is required. In this paper, we devise methods for constructing such a transition based on linear programming and network theory. We use a so-called clustering-difference graph to model the desired transformation and provide methods for decomposing the graph into a sequence of elementary moves that accomplishes the transformation. These moves are equivalent to the edge directions, or circuits, of the underlying partition polytopes. Therefore, in addition to a conceptually new metric for measuring the distance between clusterings, we provide new bounds on the circuit diameter of these partition polytopes." @default.
• W2938077962 created "2019-04-25" @default.
• W2938077962 creator A5005057455 @default.
• W2938077962 creator A5090639756 @default.
• W2938077962 date "2019-04-10" @default.
• W2938077962 modified "2023-09-27" @default.
• W2938077962 title "Constructing Clustering Transformations" @default.
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• W2938077962 hasPublicationYear "2019" @default.
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