Applies k-Medoids algorithm on the input table. Starting with a random initialization of the medoids, it iteratively performs an exhaustive search on the input data by determining the cost for swapping any medoid with any input data row. It then replaces the medoid with the data row that reduces the cost most unless no more cost reduction is possible (in which case it terminates) or the maximum number of iterations are run (or the node is canceled in the view). The costs are determined by either using a pre-computed distance matrix given (Port 0) or with the usage of a connected distance measure (Port 1).
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