The following node is available in the Open Source KNIME predictive analytics and data mining platform version 2.7.1. Discover over 1000 other nodes, as well as enterprise functionality at http://knime.com.

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). The current implementation aborts after 20 iterations.

Dialog Options

Partition Count (k)
Enter the number of partitions (must be greater than 1)
Distance Column
Select the column containing the distance values.
Chunk Size
How many rows to consider at once. This option has no effect on the output but only influence the runtime (larger chunk size resulting in more memory consumption but faster execution).
Use static seed
Seed used for random initialization. The random initialization has no practical impact on the clustering result (only for theoretical corner cases). If disabled, a "random" random seed is used.
Output relative distances to medoids
If selected, append additional columns to first output table, which reflect the relative distances to each of the medoids. The smaller the value the higher the membership to the respective partition. The values in the new columns sum to 1.
Choke on asymmetric distances
If selected, the node will fail when the input contains distance vectors that are marked as (potentially) not symmetric. Asymmetric distances may lead to infinite loops (due to alternating minimal). In most cases you should leave this box selected.

Ports

Input Ports
0 Table containing the distance matrix
Output Ports
0 Input table with additional column containing the partitioning information and the winner partition.
1 Medoid vectors (from input table) along with the partition size.
This node is contained in KNIME Distance Matrix Extension provided by KNIME GmbH, Konstanz, Germany.