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A note on the formal implementation of the \(K\)-means algorithm with hard positive and negative constraints. (English) Zbl 07300773

Summary: The paper discusses a new approach for incorporating hard constraints into the \(K\)-means algorithm for semi-supervised clustering. An analytic modification of the objective function of \(K\)-means is proposed that has not been previously considered in the literature.

MSC:

62H30 Classification and discrimination; cluster analysis (statistical aspects)

Software:

MixSim
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References:

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