rcorrmatrix
(Package: clusterGeneration) :
GENERATE A RANDOM CORRELATION MATRIX BASED ON RANDOM PARTIAL CORRELATIONS
Generate a random correlation matrix based on random partial correlations.
● Data Source:
CranContrib
● Keywords: cluster
● Alias: rcorrmatrix
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Plot all clusters in a 2-D projection space.
● Data Source:
CranContrib
● Keywords: cluster
● Alias: viewClusters
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Separation information matrix containing the nearest neighbor and farthest neighbor of each cluster.
● Data Source:
CranContrib
● Keywords: cluster
● Alias: nearestNeighborSepVal
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getSepProj
(Package: clusterGeneration) :
OPTIMAL PROJECTION DIRECTION AND CORRESPONDING SEPARATION INDEX FOR PAIRS OF CLUSTERS
Optimal projection direction and corresponding separation index for pairs of clusters.
● Data Source:
CranContrib
● Keywords: cluster
● Alias: getSepProj, getSepProjData, getSepProjTheory
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sepIndex
(Package: clusterGeneration) :
MEASURE THE MAGNITUDE OF THE GAP OR SPARSE AREA BETWEEN A PAIR OF CLUSTERS ALONG THE SPECIFIED PROJECTION DIRECTION
Measure the magnitude of the gap or sparse area between a pair of clusters (or cluster distributions) along the specified projection direction.
● Data Source:
CranContrib
● Keywords: cluster
● Alias: sepIndex, sepIndexData, sepIndexTheory
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plot2DProjection
(Package: clusterGeneration) :
PLOT A PAIR OF CLUSTERS ALONG A 2-D PROJECTION SPACE
Plot a pair of clusters along a 2-D projection space.
● Data Source:
CranContrib
● Keywords: cluster
● Alias: plot2DProjection
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Generate a positive definite matrix/covariance matrix.
● Data Source:
CranContrib
● Keywords: cluster
● Alias: genPositiveDefMat
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plot1DProjection
(Package: clusterGeneration) :
PLOT A PAIR OF CLUSTERS AND THEIR DENSITY ESTIMATES, WHICH ARE PROJECTED ALONG A SPECIFIED 1-D PROJECTION DIRECTION
Plot a pair of clusters and their density estimates, which are projected along a specified 1-D projection direction.
● Data Source:
CranContrib
● Keywords: cluster
● Alias: plot1DProjection
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simClustDesign
(Package: clusterGeneration) :
DESIGN FOR RANDOM CLUSTER GENERATION WITH SPECIFIED DEGREE OF SEPARATION
Generating data sets via a factorial design, which has factors: degree of separation, number of clusters, number of non-noisy variables, number of noisy variables. The separation between any cluster and its nearest neighboring clusters can be set to a specified value. The covariance matrices of clusters can have arbitrary diameters, shapes and orientations.
● Data Source:
CranContrib
● Keywords: cluster
● Alias: simClustDesign
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genRandomClust
(Package: clusterGeneration) :
RANDOM CLUSTER GENERATION WITH SPECIFIED DEGREE OF SEPARATION
Generate cluster data sets with specified degree of separation. The separation between any cluster and its nearest neighboring cluster can be set to a specified value. The covariance matrices of clusters can have arbitrary diameters, shapes and orientations.
● Data Source:
CranContrib
● Keywords: cluster
● Alias: genRandomClust
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