FarthestFirst (3.7)

Learner

Cluster data using the FarthestFirst algorithm. For more information see: Hochbaum, Shmoys (1985)

A best possible heuristic for the k-center problem.Mathematics of Operations Research.

10(2):180-184.

Sanjoy Dasgupta: Performance Guarantees for Hierarchical Clustering.In: 15th Annual Conference on Computational Learning Theory, 351-363, 2002.

Notes:

- works as a fast simple approximate clusterer- modelled after SimpleKMeans, might be a useful initializer for it

(based on WEKA 3.7)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify classifier-specific parameters.

Input Ports

  1. Type: Data
    Training data

Output Ports

  1. Type: Weka 3.7 Cluster
    Trained model

Extension

This node is part of the extension

KNIME Weka Data Mining Integration (3.7)

v4.0.0

Short Link

Drag node into KNIME Analytics Platform