FarthestFirst
Package
weka.clusterers
Synopsis
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
Options
The table below describes the options available for FarthestFirst.
Option |
Description |
|---|---|
numClusters |
set number of clusters |
seed |
The random number seed to be used. |
Capabilities
The table below describes the capabilites of FarthestFirst.
Capability |
Supported |
|---|---|
Class |
No class |
Attributes |
Missing values, Date attributes, Unary attributes, Empty nominal attributes, Binary attributes, Nominal attributes, Numeric attributes |
Min # of instances |
1 |