varPro (version 1.0.1)

clusterpro: ClusterPro for Unsupervised Data Visualization

Description

ClusterPro for unsupervised data visualization using Varpro rules.

Usage

clusterpro(data,
           method = c("auto", "unsupv", "rnd"),
           ntree = 100, nodesize = NULL,
           max.rules.tree = 40, max.tree = 40,
           papply = mclapply, verbose = FALSE, seed = NULL, ...)

Value

Object containing transformed data which can be passed to standard visualization tools.

Arguments

data

Data frame containing the unsupervised data.

method

Type of forest used. Options are "auto" (auto-encoder), "unsupv" (unsupervised analysis), and "rnd" (pure random forest).

ntree

Number of trees to grow.

nodesize

Minimum terminal node size. If not specified, the value is chosen by an internal function based on sample size and data dimension.

max.rules.tree

Maximum number of rules per tree.

max.tree

Maximum number of trees used to extract rules.

papply

Parallel apply method; typically mclapply or lapply.

verbose

Print verbose output?

seed

Seed for reproducibility.

...

Additional arguments passed to uvarpro.

Author

Hemant Ishwaran

Details

Unsupervised data visualization tool based on the VarPro framework. For each VarPro rule and its complementary region, a two-class analysis is performed to estimate regression coefficients that quantify variable importance relative to the release variable. These coefficients are then used to scale the centroids of the two regions.

The resulting scaled centroids from all rule pairs form an enhanced learning data set for the release variable. This transformed data can be passed to standard visualization tools (e.g., UMAP or t-SNE) to explore structure and relationships in the original high-dimensional space.

See Also

plot.clusterpro uvarpro

Examples

# \donttest{

##------------------------------------------------------------------
##
## V-cluster simulation
##
##------------------------------------------------------------------


vcsim <- function(m=500, p=9, std=.2) {
  p <- max(p, 2)
  n <- 2 * m
  x <- runif(n, 0, 1)
  y <- rep(NA, n)
  y[1:m] <- x[1:m] + rnorm(m, sd = std)
  y[(m+1):n] <- -x[(m+1):n] + rnorm(m, sd = std)
  data.frame(x = x,
             y = y,
             z = matrix(runif(n * p, 0, 1), n))
}

dvc <- vcsim()
ovc <- clusterpro(dvc)
oldpar<-par(mfrow=c(3,3));plot(ovc,1:9);par(oldpar)
oldpar<-par(mfrow=c(3,3));plot(ovc,1:9,col.names="x");par(oldpar)

##------------------------------------------------------------------
##
## 4-cluster simulation
##
##------------------------------------------------------------------


if (library("MASS", logical.return=TRUE)) {


fourcsim <- function(n=500, sigma=2) {
  
  cl1 <- mvrnorm(n,c(0,4),cbind(c(1,0),c(0,sigma)))
  cl2 <- mvrnorm(n,c(4,0),cbind(c(1,0),c(0,sigma)))
  cl3 <- mvrnorm(n,c(0,-4),cbind(c(1,0),c(0,sigma)))
  cl4 <- mvrnorm(n,c(-4,0),cbind(c(1,0),c(0,sigma)))
  dta <- data.frame(rbind(cl1,cl2,cl3,cl4))
  colnames(dta) <- c("x","y")
  data.frame(dta, noise=matrix(rnorm((n*4)*20),ncol=20))

}

d4c <- fourcsim()
o4c <- clusterpro(d4c)
oldpar<-par(mfrow=c(2,2));plot(o4c,1:4);par(oldpar)

}

##------------------------------------------------------------------
##
## latent variable simulation
##
##------------------------------------------------------------------

lvsim <- function(n=1000, q=2, qnoise=15, noise=FALSE)  {
  w <- rnorm(n)
  x <- rnorm(n)
  y <- rnorm(n)
  z <- rnorm(n)
  ei <- matrix(rnorm(n * q * 4, sd = sqrt(.1)), ncol = q * 4)
  e1 <- rnorm(n, sd = sqrt(.4))
  e2 <- rnorm(n, sd = sqrt(.4))
  wi <- w + ei[, 1:q]
  xi <- x + ei[, (q+1):(2*q)]
  yi <- y + ei[, (2*q+1):(3*q)]
  zi <- z + ei[, (3*q+1):(4*q)]
  h1 <- w + x + e1
  h2 <- y + z + e2
  dta <- data.frame(w=w,wi=wi,x=x,xi=xi,y=y,yi=yi,z=z,zi=zi,h1=h1,h2=h2)
  if (noise) {
    dta <- data.frame(dta, noise = matrix(rnorm(n * qnoise), ncol = qnoise))
  }
  dta
}

dlc <- lvsim()
olc <- clusterpro(dlc)
oldpar<-par(mfrow=c(4,4));plot(olc,col.names="w");par(oldpar)


##------------------------------------------------------------------
##
## Glass mlbench data 
##
##------------------------------------------------------------------

data(Glass, package = "mlbench")
dg <- Glass

## with class label
og <- clusterpro(dg)
oldpar<-par(mfrow=c(4,4));plot(og,1:16);par(oldpar)

## without class label
dgU <- Glass; dgU$Type <- NULL
ogU <- clusterpro(dgU)
oldpar<-par(mfrow=c(3,3));plot(ogU,1:9);par(oldpar)

# }