Introduction to iClusterVB"

In iClusterVB: Fast Integrative Clustering and Feature Selection for High Dimensional Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

iClusterVB

iClusterVB allows for fast integrative clustering and feature selection for high dimensional data.

Using a variational Bayes approach, its key features - clustering of mixed-type data, automated determination of the number of clusters, and feature selection in high-dimensional settings - address the limitations of traditional clustering methods while offering an alternative and potentially faster approach than MCMC algorithms, making iClusterVB a valuable tool for contemporary data analysis challenges.

There is a simulated dataset included as a list in the package that we can use to illustrate iClusterVB.

Data pre-processing

library(iClusterVB)

# sim_data comes with the iClusterVB package.
dat1 <- list(
  gauss_1 = sim_data$continuous1_data[c(1:20, 61:80, 121:140, 181:200), 1:75],
  gauss_2 = sim_data$continuous2_data[c(1:20, 61:80, 121:140, 181:200), 1:75],
  poisson_1 = sim_data$count_data[c(1:20, 61:80, 121:140, 181:200), 1:75])


dist <- c(
  "gaussian", "gaussian",
  "poisson"
)

Running the model

fit_iClusterVB <- iClusterVB(
  mydata = dat1,
  dist = dist,
  K = 4,
  initial_method = "VarSelLCM",
  VS_method = 1,
  max_iter = 50
)

Summary of the Model

# We can obtain a summary using summary()
summary(fit_iClusterVB)

Generic Plots

plot(fit_iClusterVB)

Probability of Inclusion Plots

# The `piplot` function can be used to visualize the probability of inclusion

piplot(fit_iClusterVB)

Heat maps to visualize the clusters

# The `chmap` function can be used to display heat maps for each data view

chmap(fit_iClusterVB, rho = 0,
      cols = c("green", "blue",
               "purple", "red"),
      scale = "none")

iClusterVB documentation built on April 3, 2025, 6:22 p.m.