mvc: Multi-View Clustering

An implementation of Multi-View Clustering (Bickel and Scheffer, 2004). Documents are generated by drawing word values from a categorical distribution for each word, given the cluster. This means words are not counted (multinomial, as in the paper), but words take on different values from a finite set of values (categorical). Thus, it implements Mixture of Categoricals EM (as opposed to Mixture of Multinomials developed in the paper), and Spherical k-Means. The latter represents documents as vectors in the categorical space.

AuthorAndreas Maunz <andreas@maunz.de>
Date of publication2014-02-24 08:05:39
MaintainerAndreas Maunz <andreas@maunz.de>
LicenseBSD_3_clause + file LICENSE
Version1.3
http://cs.maunz.de

View on CRAN

Functions

agreementRateBinM Man page
assignFinIdxPerClSkm Man page
assignIdxPerClMBinEM Man page
checkViews Man page
conceptIndicesSkm Man page
conceptVectorsSkm Man page
consensusMeansPerClVSkm Man page
dbern Man page
dcat Man page
estLogPxBernGthetaJ Man page
estLogPxCatGthetaJ Man page
logsum Man page
mApplyBern Man page
mApplyCat Man page
mvcmb Man page
mvcsph Man page
oFMixBinEM Man page
oFSkm Man page
rowWUL Man page
toyView1 Man page
toyView2 Man page
toyViews Man page
UL Man page
vectorLength Man page
viewsClasses Man page

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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