Multi-View Clustering


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Documentation for package ‘mvc’ version 1.3

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agreementRateBinM Agreement rate by maximum posterior values.
assignFinIdxPerClSkm Assign final indices to means that have the smallest angle.
assignIdxPerClMBinEM Assign final indices to data by maximum posterior value.
checkViews Check views for consistency...
conceptIndicesSkm Calculate partitions (concept indices) by assigning each vector to the closest concept vector.
conceptVectorsSkm Calculate concept vectors for Spherical k-Means as unit length sum of vectors of the k clusters.
consensusMeansPerClVSkm Calculate means per Cluster and view for Spherical k-Means by using a consensus approach.
dbern Calculate Bernoulli likelihood...
dcat Calculate categorical likelihood...
estLogPxBernGthetaJ Estimate log document probabilites given specific Bernoulli parameters...
estLogPxCatGthetaJ Estimate log document probabilites given specific Categorical parameters...
logsum Computes the cumulative sum in terms of logarithmic in- and output...
mApplyBern Calculate Bernoulli likelihood row-wise for binary events...
mApplyCat Calculate categorical likelihood row-wise for categorical events...
mvcmb Multi-View Clustering using mixture of categoricals EM.
mvcsph Multi-View Clustering using Spherical k-Means for categorical data.
oFMixBinEM objective function for mixture of binomials EM:...
oFSkm Objective Function (sum of cosines)...
rowWUL Unit length of all vectors row-wise...
toyView1 Toy View 1...
toyView2 Toy View 2...
toyViews Toy Views...
UL Unit length for vector...
vectorLength Euclidean length of vector...
viewsClasses Counts unique values in both views...