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Last data update: 2014.03.03

R: KpartitionNJW function

KpartitionNJW

R Documentation

KpartitionNJW function

Description

Perform spectral classification on the similarity matrix of a dataset (Ng et al. (2001) algorithm), using kmeans algorithm on data projected in the space of its K first eigen vectors.

Usage

KpartitionNJW(similarity, K)

Arguments

`similarity`	matrix of similarity.
`K`	number of clusters.

Value

The function returns a list containing:

`label`	vector of cluster sequencing.
`centres`	matrix of cluster centers in the space of the K first normalised eigen vectors.
`vecteursPropresProjK`	matrix containing, in columns, the K first normalised eigen vectors of the similarity matrix.
`valeursPropresK`	vector containing the K first eigen values of the similarity matrix.
`vecteursPropres`	matrix containing, in columns, eigen vectors of the similarity matrix.
`valeursPropres`	vector containing eigen values of the similarity matrix.
`inertieZ`	vector of within-cluster sum of squares, one component per cluster.

References

Ng Andrew, Y., M. I. Jordan, and Y. Weiss. "On spectral clustering: analysis and an algorithm [C]." Advances in Neural Information Processing Systems (2001).

Examples


#####
x <- rbind(matrix(rnorm(100, mean = 0, sd = 0.3), ncol = 2), 
           matrix(rnorm(100, mean = 2, sd = 0.3), ncol = 2), 
           matrix(rnorm(100, mean = 4, sd = 0.3), ncol = 2))

similarity<-ZPGaussianSimilarity(x,7)
similarity=similarity%*%t(similarity)
sp<-KpartitionNJW(similarity,3)
plot(x,col=sp$label)

#####    
x <- rbind(data.frame(x=1:100+(runif(100)-0.5)*2,y=runif(100)/5), 
           data.frame(x=1:100+(runif(100)-0.5)*2,y=runif(100)/5+1),
           data.frame(x=1:100+(runif(100)-0.5)*2,y=runif(100)/5+2))

similarity<-ZPGaussianSimilarity(x,7)
similarity=similarity%*%t(similarity)
sp<-KpartitionNJW(similarity,3)
plot(x,col=sp$label)

#####
x=(runif(1000)*4)-2;y=(runif(1000)*4)-2
keep<-which((x**2+y**2<0.5)|(x**2+y**2>1.5**2 & x**2+y**2<2**2 ))
data<-data.frame(x,y)[keep,]

similarity=ZPGaussianSimilarity(data, 7)
similarity=similarity%*%t(similarity)
sp<-KpartitionNJW(similarity,2)

plot(data,col=sp$label)