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).
You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.
A zipped version of the software site can be downloaded here.
Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to email@example.com, follow @NodePit on Twitter, or chat on Gitter!
Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.