| AbstractKMeansInitialization |
Abstract base class for common k-means initializations.
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| AbstractKMeansInitialization.Par |
Parameterization class.
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| AFKMC2 |
AFK-MC² initialization
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| AFKMC2.Instance |
Abstract instance implementing the weight handling.
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| AFKMC2.Par |
Parameterization class.
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| FarthestPoints |
K-Means initialization by repeatedly choosing the farthest point (by the
minimum distance to earlier points).
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| FarthestPoints.Par |
Parameterization class.
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| FarthestSumPoints |
K-Means initialization by repeatedly choosing the farthest point (by the
sum of distances to previous objects).
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| FirstK |
Initialize K-means by using the first k objects as initial means.
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| KMC2 |
K-MC² initialization
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| KMC2.Instance |
Abstract instance implementing the weight handling.
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| KMC2.Par |
Parameterization class.
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| KMeansInitialization |
Interface for initializing K-Means
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| KMeansPlusPlus |
K-Means++ initialization for k-means.
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| KMeansPlusPlus.Instance |
Abstract instance implementing the weight handling.
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| KMeansPlusPlus.NumberVectorInstance |
Instance for k-means, number vector based.
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| Ostrovsky |
Ostrovsky initial means, a variant of k-means++ that is expected to give
slightly better results on average, but only works for k-means and not for,
e.g., PAM (k-medoids).
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| Predefined |
Run k-means with prespecified initial means.
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| RandomlyChosen |
Initialize K-means by randomly choosing k existing elements as initial
cluster centers.
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| RandomNormalGenerated |
Initialize k-means by generating random vectors (normal distributed
with \(N(\mu,\sigma)\) in each dimension).
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| RandomUniformGenerated |
Initialize k-means by generating random vectors (uniform, within the value
range of the data set).
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| SampleKMeans |
Initialize k-means by running k-means on a sample of the data set only.
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| SphericalAFKMC2 |
Spherical K-Means++ initialization with markov chains.
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| SphericalKMeansPlusPlus |
Spherical K-Means++ initialization for k-means.
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