Class Summary |
AlgVector |
Class for performing operations on an algebraic vector
of floating-point values. |
Cluster |
|
Clusterer |
Abstract clusterer. |
ClusterEvaluation |
Class for evaluating clustering models. |
Cobweb |
Class implementing the Cobweb and Classit clustering algorithms. |
DistributionClusterer |
Abstract clustering model that produces (for each test instance)
an estimate of the membership in each cluster
(ie. |
DistributionMetaClusterer |
Class for wrapping a Clusterer to make it return a 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: |
HAC |
|
InstancePair |
Class for handling a pair of instances, in terms of indices of instances in an Instances set |
MPCKMeans |
Pairwise constrained k means clustering class. |
PCKMeans |
Pairwise constrained k means clustering class. |
PCSoftKMeans |
Pairwise constrained k means clustering class. |
SeededKMeans |
Seeded k means clustering class. |
Seeder |
|
SemiSupClustererEvaluation |
Class for evaluating clustering models - extends ClusterEvaluation.java |
SimpleKMeans |
Simple k means clustering class. |
XMeans |
XMeans clustering class. |