|
Class Summary |
| Clusterer |
Abstract clusterer. |
| ClusterEvaluation |
Class for evaluating clustering models. |
| Cobweb |
Class implementing the Cobweb and Classit clustering algorithms. |
| DensityBasedClusterer |
Abstract clustering model that produces (for each test instance)
an estimate of the membership in each cluster
(ie. a probability distribution). |
| EM |
Simple EM (expectation maximisation) class. |
| FarthestFirst |
Implements the "Farthest First Traversal Algorithm" by
Hochbaum and Shmoys 1985: A best possible heuristic for the
k-center problem, Mathematics of Operations Research, 10(2):180-184,
as cited by Sanjoy Dasgupta "performance guarantees for hierarchical
clustering", colt 2002, sydney
works as a fast simple approximate clusterer
modelled after SimpleKMeans, might be a useful initializer for it
Valid options are:
-N
Specify the number of clusters to generate. |
| MakeDensityBasedClusterer |
Class for wrapping a Clusterer to make it return a distribution and density. |
| SimpleKMeans |
Simple k means clustering class.
|