| weka.clusterers |
|
| Java Source File Name | Type | Comment |
| CheckClusterer.java | Class | Class for examining the capabilities and finding problems with
clusterers. |
| Clusterer.java | Class | Abstract clusterer. |
| ClusterEvaluation.java | Class | Class for evaluating clustering models.
Valid options are:
-t name of the training file
Specify the training file. |
| Cobweb.java | Class |
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.java | Class |
Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. |
| DensityBasedClusterer.java | Class | Abstract clustering model that produces (for each test instance)
an estimate of the membership in each cluster
(ie. |
| EM.java | Class |
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.java | Class |
Cluster data using the FarthestFirst algorithm.
For more information see:
Hochbaum, Shmoys (1985). |
| FilteredClusterer.java | Class |
Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter. |
| MakeDensityBasedClusterer.java | Class |
Class for wrapping a Clusterer to make it return a distribution and density. |
| NumberOfClustersRequestable.java | Interface | |
| OPTICS.java | Class |
Mihael Ankerst, Markus M. |
| RandomizableClusterer.java | Class | Abstract utility class for handling settings common to randomizable
clusterers. |
| RandomizableDensityBasedClusterer.java | Class | Abstract utility class for handling settings common to randomizable
clusterers. |
| RandomizableSingleClustererEnhancer.java | Class | Abstract utility class for handling settings common to randomizable
clusterers. |
| SimpleKMeans.java | Class |
Cluster data using the k means algorithm
Valid options are:
-N <num>
number of clusters. |
| SingleClustererEnhancer.java | Class | Meta-clusterer for enhancing a base clusterer. |
| UpdateableClusterer.java | Interface | Interface to incremental cluster models that can learn using one instance
at a time. |
| XMeans.java | Class |
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. |