Package | Description |
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de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical | |
tutorial.clustering |
Classes from the tutorial on implementing a custom k-means variation.
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Modifier and Type | Class and Description |
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class |
NaiveAgglomerativeHierarchicalClustering<O,D extends NumberDistance<D,?>>
This tutorial will step you through implementing a well known clustering
algorithm, agglomerative hierarchical clustering, in multiple steps.
|
class |
SLINK<O,D extends Distance<D>>
Implementation of the efficient Single-Link Algorithm SLINK of R.
|
Modifier and Type | Field and Description |
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private HierarchicalClusteringAlgorithm<D> |
ExtractFlatClusteringFromHierarchy.algorithm
Clustering algorithm to run to obtain the hierarchy.
|
(package private) HierarchicalClusteringAlgorithm<D> |
ExtractFlatClusteringFromHierarchy.Parameterizer.algorithm
The hierarchical clustering algorithm to run.
|
Constructor and Description |
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ExtractFlatClusteringFromHierarchy(HierarchicalClusteringAlgorithm<D> algorithm,
D threshold,
ExtractFlatClusteringFromHierarchy.OutputMode outputmode,
boolean singletons)
Constructor.
|
ExtractFlatClusteringFromHierarchy(HierarchicalClusteringAlgorithm<D> algorithm,
int minclusters,
ExtractFlatClusteringFromHierarchy.OutputMode outputmode,
boolean singletons)
Constructor.
|
Modifier and Type | Class and Description |
---|---|
class |
NaiveAgglomerativeHierarchicalClustering4<O,D extends NumberDistance<D,?>>
This tutorial will step you through implementing a well known clustering
algorithm, agglomerative hierarchical clustering, in multiple steps.
|