PAC (version 1.1.4)

PAC: Partition Assisted Clustering PAC 1) utilizes dsp or bsp-ll to recursively partition the data space and 2) applies a short round of kmeans style postprocessing to efficiently output clustered labels of data points.

Description

Partition Assisted Clustering PAC 1) utilizes dsp or bsp-ll to recursively partition the data space and 2) applies a short round of kmeans style postprocessing to efficiently output clustered labels of data points.

Usage

PAC(data, K, maxlevel = 40, method = "dsp", max.iter = 50)

Arguments

data

a n x p data matrix

K

number of final clusters in the output

maxlevel

the maximum level of the partition

method

partition method, either "dsp(discrepancy based partition)", or "bsp(bayesian sequantial partition)"

max.iter

maximum iteration for the kmeans step

Value

y cluter labels for the input

Examples

# NOT RUN {
n = 5e3                       # number of observations
p = 1                         # number of dimensions
K = 3                         # number of clusters
w = rep(1,K)/K                # component weights
mu <- c(0,2,4)                # component means
sd <- rep(1,K)/K              # component standard deviations
g <- sample(1:K,prob=w,size=n,replace=TRUE)   # ground truth for clustering
X <- as.matrix(rnorm(n=n,mean=mu[g],sd=sd[g]))
y <- PAC(X, K)
print(fmeasure(g,y))
# }