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.

Installation

You can install iClusterVB from CRAN with:

install.packages("iClusterVB")

You can install the development version of iClusterVB from GitHub with:

# install.packages("devtools")
devtools::install_github("AbdalkarimA/iClusterVB")

iClusterVB - The Main Function

Mandatory arguments

• mydata: A list of length R, where R is the number of datasets, containing the input data.

  • Note: For categorical data, 0’s must be re-coded to another, non-0 value.

• dist: A vector of length R specifying the type of data or distribution. Options include: "gaussian" (for continuous data), "multinomial" (for binary or categorical data), and "poisson" (for count data).

Optional arguments

• K: The maximum number of clusters, with a default value of 10. The algorithm will converge to a model with dominant clusters, removing redundant clusters and automating the process of determining the number of clusters.

• initial_method: The method for the initial cluster allocation, which the iClusterVB algorithm will then use to determine the final cluster allocation. Options include "VarSelLCM" (default) for VarSelLCM, "random" for a random sample, "kproto" for k-prototypes, "kmeans" for k-means (continuous data only), "mclust" for mclust (continuous data only), or "lca" for poLCA (categorical data only).

• VS_method: The feature selection method. The options are 0 (default) for clustering without feature selection and 1 for clustering with feature selection

• initial_cluster: The initial cluster membership. The default is NULL, which uses initial_method for initial cluster allocation. If it is not NULL, it will overwrite the previous initial values setting for this parameter.

• initial_vs_prob: The initial feature selection probability, a scalar. The default is NULL, which assigns a value of 0.5.

• initial_fit: Initial values based on a previously fitted iClusterVB model (an iClusterVB object). The default is NULL.

• initial_omega: Customized initial values for feature inclusion probabilities. The default is NULL. If the argument is not NULL, it will overwrite the previous initial values setting for this parameter. If VS_method = 1, initial_omega is a list of length R, and each element of the list is an array with dim=c(N,p[[r]])). N is the sample size and p[[r]] is the number of features for dataset r, r = 1,…,R.

• initial_hyper_parameters: A list of the initial hyper-parameters of the prior distributions for the model. The default is NULL, which assigns alpha_00 = 0.001, mu_00 = 0,
s2_00 = 100, a_00 = 1, b_00 = 1, kappa_00 = 1, u_00 = 1, v_00 = 1. These are