k-Medoids

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).

Input ports

1. Type: Table
   Input Table, with opt. Distance Matrix
   Table containing the optional distance matrix.
2. Type: Distance Measure
   Distance Measure
   Optional distance measure, which renders the distance matrix at Port 0 unnecessary.

Output ports

1. Type: Table
   Clustered input
   Input table with additional column containing the partitioning information and the winner partition.
2. Type: Table
   Medoids and Size
   Medoid vectors (from input table) along with the partition size.