Package org.ojalgo.data.cluster
Class GeneralisedKMeans<T>
java.lang.Object
org.ojalgo.data.cluster.GeneralisedKMeans<T>
- All Implemented Interfaces:
ClusteringAlgorithm<T>
Contains the outline of the k-means algorithm, but designed for customisation.
- Works with any type of data
- Allows for custom distance calculations
- Allows for custom centroid initialisation and updating
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Field Summary
FieldsModifier and TypeFieldDescriptionprivate static final NumberContext
private final Function
<Collection<T>, List<T>> private final Function
<Collection<T>, T> private final ToDoubleBiFunction
<T, T> -
Constructor Summary
ConstructorsConstructorDescriptionGeneralisedKMeans
(Function<Collection<T>, List<T>> centroidInitialiser, Function<Collection<T>, T> centroidUpdater, ToDoubleBiFunction<T, T> distanceCalculator) You have to configure how distances are measured and how centroids are derived. -
Method Summary
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Field Details
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ACCURACY
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myCentroidUpdater
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myDistanceCalculator
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myCentroidInitialiser
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Constructor Details
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GeneralisedKMeans
public GeneralisedKMeans(Function<Collection<T>, List<T>> centroidInitialiser, Function<Collection<T>, T> centroidUpdater, ToDoubleBiFunction<T, T> distanceCalculator) You have to configure how distances are measured and how centroids are derived.- Parameters:
centroidInitialiser
- The initialisation function should return a list of k centroids. This function determines 'K'.centroidUpdater
- The update function should return a new centroid based on a collection of points (the set of items in a cluster).distanceCalculator
- A function that calculates the distance between two points.
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Method Details
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cluster
- Specified by:
cluster
in interfaceClusteringAlgorithm<T>
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