computeCSC: Constrained Spectral Clustering In RclusTool: Graphical Toolbox for Clustering and Classification of Data Frames

Description

Perform Constrained Spectral Clustering from a similarity matrix computation.

Usage

 ```1 2 3 4 5 6 7 8``` ```computeCSC( x, K = 0, K.max = 20, mustLink = list(), cantLink = list(), alphas = seq(from = 0, to = 1, length = 100) ) ```

Arguments

 `x` matrix of raw data (point by line). `K` number of clusters. If K=0 (default), this number is automatically computed thanks to the Elbow method. `K.max` maximal number of clusters (K.Max=20 by default). `mustLink` list of ML (must-link) constrained pairs. `cantLink` list of CNL (cannot-link) constrained pairs. `alphas` numeric vector for the weight of constraints considered.

Details

computeCSC performs Constrained Spectral Clustering from a similarity matrix computation

Value

res.csc results obtained from KwaySSSC algorithm.

See Also

`computeSemiSupervised`, `KwaySSSC`

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```dat <- 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)) tf <- tempfile() write.table(dat, tf, sep=",", dec=".") x <- importSample(file.features=tf, dir.save=tempdir()) ML=list(c(sel="10",mem="20")) CNL=list(c(sel="1",mem="140")) res.csc <- computeCSC(x\$features\$preprocessed\$x, K=0, mustLink=ML, cantLink=CNL) plot(dat[,1], dat[,2], type = "p", xlab = "x", ylab = "y", col = res.csc\$label, main = "Constrained Spectral clustering") ```

RclusTool documentation built on Feb. 4, 2020, 5:08 p.m.