measureConstraintsOk: Rates of constraints satisfaction

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

Rates of constraints satisfaction

Description

Calculate the rates of ML and CNL constraints satisfaction in a clustering result.

Usage

measureConstraintsOk(label, list.ML = list(), list.CNL = list())

Arguments

label	vector of labels.
list.ML	list of ML (must-link) constrained pairs.
list.CNL	list of CNL (cannot-link) constrained pairs.

Details

measureConstraintsOk returns the rates of constraints satisfaction

Value

The function returns a list containing:

ML	rate of ML (must-link) constraints satisfaction.
CNL	rate of CNL (cannot-link) constraints satisfaction.

Examples

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))
           
ML <- list()
ML[[1]] <- c(sel="10",mem="20")
ML[[2]] <- c(sel="60",mem="70")

CNL <- list()
CNL[[1]] <- c(sel="30",mem="80")
CNL[[2]] <- c(sel="90",mem="120")

sim <- computeGaussianSimilarity(dat, 1)
res <- KwaySSSC(sim, K=0, list.ML=ML, list.CNL=CNL)

measureConstraintsOk(res$label, list.ML=ML, list.CNL=CNL)

RclusTool documentation built on Aug. 29, 2022, 9:07 a.m.