| Interface | Description |
|---|---|
| Clusterer |
Interface for clusterers.
|
| DensityBasedClusterer |
Interface for clusterers that can estimate the density for a given instance.
|
| NumberOfClustersRequestable |
Interface to a clusterer that can generate a requested number of
clusters
|
| UpdateableClusterer |
Interface to incremental cluster models that can learn using one instance at
a time.
|
| Class | Description |
|---|---|
| AbstractClusterer |
Abstract clusterer.
|
| AbstractDensityBasedClusterer |
Abstract clustering model that produces (for each test instance)
an estimate of the membership in each cluster
(ie.
|
| CheckClusterer |
Class for examining the capabilities and finding problems with
clusterers.
|
| CLOPE |
Yiling Yang, Xudong Guan, Jinyuan You: CLOPE: a fast and effective clustering algorithm for transactional data.
|
| ClusterEvaluation |
Class for evaluating clustering models.
Valid options are:
-t name of the training file
Specify the training file. |
| Cobweb |
Class implementing the Cobweb and Classit clustering algorithms.
Note: the application of node operators (merging, splitting etc.) in terms of ordering and priority differs (and is somewhat ambiguous) between the original Cobweb and Classit papers. |
| DBSCAN |
Basic implementation of DBSCAN clustering algorithm that should *not* be used as a reference for runtime benchmarks: more sophisticated implementations exist! Clustering of new instances is not supported.
|
| EM |
Simple EM (expectation maximisation) class.
EM assigns a probability distribution to each instance which indicates the probability of it belonging to each of the clusters. |
| FarthestFirst |
Cluster data using the FarthestFirst algorithm.
For more information see: Hochbaum, Shmoys (1985). |
| FilteredClusterer |
Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter.
|
| HierarchicalClusterer |
Hierarchical clustering class.
|
| MakeDensityBasedClusterer |
Class for wrapping a Clusterer to make it return a distribution and density.
|
| OPTICS |
Basic implementation of OPTICS clustering algorithm that should *not* be used as a reference for runtime benchmarks: more sophisticated implementations exist! Clustering of new instances is not supported.
|
| RandomizableClusterer |
Abstract utility class for handling settings common to randomizable
clusterers.
|
| RandomizableDensityBasedClusterer |
Abstract utility class for handling settings common to randomizable
clusterers.
|
| RandomizableSingleClustererEnhancer |
Abstract utility class for handling settings common to randomizable
clusterers.
|
| sIB |
Cluster data using the sequential information bottleneck algorithm.
Note: only hard clustering scheme is supported. |
| SimpleKMeans |
Cluster data using the k means algorithm
Valid options are:
|
| SingleClustererEnhancer |
Meta-clusterer for enhancing a base clusterer.
|
| XMeans |
Cluster data using the X-means algorithm.
X-Means is K-Means extended by an Improve-Structure part In this part of the algorithm the centers are attempted to be split in its region. |