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Multi-View Clustering

Implementation of: S Bickel and T Scheffer: Multi-View Clustering, Proceedings of the Fourth IEEE International Conference on Data Mining, pages 19-26.

Contents

  • Multi-View Clustering using Spherical k-Means for categorical data.
  • Multi-View Clustering using mixture of categoricals EM.

Stable Version: Installation from CRAN

  • Start R
  • Run install.packages('mvc')

Note: The manual is available as PDF on CRAN.

Development Version: Installation from source

  • Download: git clone git://github.com/amaunz/mvc
  • Check out development version: cd mvc; git checkout development; cd -
  • Check: R CMD check mvc
  • Install: R CMD install mvc

Note: The check command creates the manual as PDF under mvc.Rcheck/mvc-manual.pdf.

Demo

Multi-View Clustering using Spherical k-Means:

library(mvc)
data(toyViews)
results=mvcsph(
   view1=toyView1,
   view2=toyView2,
   nthresh=20,
   k=4,
   startView="view1"
)
print(results$finalIndices)

Multi-View Clustering using mixture of categoricals EM:

library(mvc)
data(toyViews)
results=mvcmb(
  view1=toyView1,
  view2=toyView2,
  nthresh=20,
  k=4,
  startView="view1"
)
print(results$finalIndices)

In both cases, the first three and the last two instances should each form a cluster.

License

BSD

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Version

Install

install.packages('mvc')

Monthly Downloads

47

Version

1.3

License

BSD_3_clause + file LICENSE

Maintainer

Andreas Maunz

Last Published

February 24th, 2014

Functions in mvc (1.3)

agreementRateBinM

Agreement rate by maximum posterior values.
mApplyBern

Calculate Bernoulli likelihood row-wise for binary events...
assignFinIdxPerClSkm

Assign final indices to means that have the smallest angle.
dbern

Calculate Bernoulli likelihood...
UL

Unit length for vector...
estLogPxCatGthetaJ

Estimate log document probabilites given specific Categorical parameters...
checkViews

Check views for consistency...
mApplyCat

Calculate categorical likelihood row-wise for categorical events...
conceptIndicesSkm

Calculate partitions (concept indices) by assigning each vector to the closest concept vector.
consensusMeansPerClVSkm

Calculate means per Cluster and view for Spherical k-Means by using a consensus approach.
oFMixBinEM

objective function for mixture of binomials EM:...
toyView2

Toy View 2...
logsum

Computes the cumulative sum in terms of logarithmic in- and output...
toyViews

Toy Views...
mvcmb

Multi-View Clustering using mixture of categoricals EM.
viewsClasses

Counts unique values in both views...
mvcsph

Multi-View Clustering using Spherical k-Means for categorical data.
estLogPxBernGthetaJ

Estimate log document probabilites given specific Bernoulli parameters...
toyView1

Toy View 1...
assignIdxPerClMBinEM

Assign final indices to data by maximum posterior value.
conceptVectorsSkm

Calculate concept vectors for Spherical k-Means as unit length sum of vectors of the k clusters.
dcat

Calculate categorical likelihood...
oFSkm

Objective Function (sum of cosines)...
vectorLength

Euclidean length of vector...
rowWUL

Unit length of all vectors row-wise...