Package | Description |
---|---|
weka.clusterers |
Modifier and Type | Class and Description |
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class |
AbstractDensityBasedClusterer
Abstract clustering model that produces (for each test instance)
an estimate of the membership in each cluster
(ie.
|
class |
CLOPE
Yiling Yang, Xudong Guan, Jinyuan You: CLOPE: a fast and effective clustering algorithm for transactional data.
|
class |
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. |
class |
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.
|
class |
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. |
class |
FarthestFirst
Cluster data using the FarthestFirst algorithm.
For more information see: Hochbaum, Shmoys (1985). |
class |
FilteredClusterer
Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter.
|
class |
HierarchicalClusterer
Hierarchical clustering class.
|
class |
MakeDensityBasedClusterer
Class for wrapping a Clusterer to make it return a distribution and density.
|
class |
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.
|
class |
RandomizableClusterer
Abstract utility class for handling settings common to randomizable
clusterers.
|
class |
RandomizableDensityBasedClusterer
Abstract utility class for handling settings common to randomizable
clusterers.
|
class |
RandomizableSingleClustererEnhancer
Abstract utility class for handling settings common to randomizable
clusterers.
|
class |
sIB
Cluster data using the sequential information bottleneck algorithm.
Note: only hard clustering scheme is supported. |
class |
SimpleKMeans
Cluster data using the k means algorithm
Valid options are:
|
class |
SingleClustererEnhancer
Meta-clusterer for enhancing a base clusterer.
|
class |
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. |
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