BossaSimi: Bossa Similarity
In TinyOpen/boclust: A Clustering Method Based on Boosting on Single Attributes

Description Usage Arguments Value Examples

Calculate the similarity matrix of Bossa scores which are obtained by boosting on single attribute.

1 2	BossaSimi(data, is.pca = TRUE, pca.sum.prop = 0.95, fix.pca.comp = FALSE, n.comp = 50, alpha = 1, pro.show = FALSE)

`data`	A data.frame or matrix(`n*p`) of original categorical data.
`is.pca`	A logical variable indicating if the Bossa scores should transformed to principle components and then calculate the similarity matrix. It is recommended when processing the ultra-dimension data.
`pca.sum.prop`	A numeric indicating how many components should be reserved in order to make this proportion of variance. The default is `pca.sum.prop = 0.95`.
`fix.pca.comp`	A numeric variable indicating whether choosing the fixed number of components or the fixed proportion of variance and the default is to choose fixed proportion.
`n.comp`	The number of components of PCA. The default is `n.comp = 50`.
`alpha`	A power scaling for Bossa scores, representing the weight of variable sigma value.
`pro.show`	A logical indicator whether show the details of the process.

An object including Bossa scores, Bossa dissimilarity and Bossa similarity(for OverlapClust.

{
## generate sparse data from the toy model of CIDR
sparse.data <- data.frame(g.1 = c(0, 5, 0, 6, 8, 6, 7, 7), g.2 = c(5, 0, 0, 0, 5, 7, 5, 7))

## with low-dimensional data, pca is uncessary
bossa.change <- BossaSimi(sparse.data, is.pca = FALSE)

## data after normalization
data.after <- bossa.change$U.score.non.pca

## similarity matrix of normalized data
data.simi <- bossa.change$bossa.simi
}