Package | Description |
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de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans |
K-means clustering and variations.
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de.lmu.ifi.dbs.elki.math |
Mathematical operations and utilities used throughout the framework.
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de.lmu.ifi.dbs.elki.math.dimensionsimilarity |
Functions to compute the similarity of dimensions (or the interestingness of the combination).
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Modifier and Type | Method and Description |
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protected boolean |
KMedoidsEM.assignToNearestCluster(ArrayDBIDs means,
Mean[] mdist,
List<? extends ModifiableDBIDs> clusters,
DistanceQuery<V,D> distQ)
Returns a list of clusters.
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Modifier and Type | Class and Description |
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class |
MeanVariance
Do some simple statistics (mean, variance) using a numerically stable online
algorithm.
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class |
MeanVarianceMinMax
Class collecting mean, variance, minimum and maximum statistics.
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Modifier and Type | Method and Description |
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static Mean[] |
Mean.newArray(int dimensionality)
Create and initialize a new array of MeanVariance
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Modifier and Type | Method and Description |
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void |
MeanVariance.put(Mean other)
Join the data of another MeanVariance instance.
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void |
Mean.put(Mean other)
Join the data of another MeanVariance instance.
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void |
MeanVarianceMinMax.put(Mean other) |
Constructor and Description |
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Mean(Mean other)
Constructor from other instance
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Modifier and Type | Method and Description |
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private void |
MCEDimensionSimilarity.divide(DBIDArrayIter it,
double[] data,
ArrayList<DBIDs> idx,
int start,
int end,
int depth,
Mean mean)
Recursive call to further subdivide the array.
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