IBclust is an R package for clustering datasets using the Information Bottleneck method and its variants. This package supports datasets with mixed-type variables (nominal, ordinal, and continuous), as well as datasets that are purely continuous or categorical. The IB approach preserves the most relevant information while forming concise and interpretable clusters, guided by principles from information theory. ## Installation
You can install the latest version of the package directly from
GitHub using devtools:
install.packages("devtools") # Install devtools if not already installed
devtools::install_github("amarkos/IBclust") # Install IBclust from GitHubBelow is a comprehensive example demonstrating how to use the package for clustering mixed-type, continuous, and categorical datasets, and displaying the results. The examples make use of the Deterministic Information Bottleneck (DIB) method for lcustering; other options include the Agglomerative IB for hierarchical clustering, the Generalised IB and the standard IB for fuzzy clustering.
library(IBclust)
# Example Mixed-Type Data
data <- data.frame(
cat_var = factor(sample(letters[1:3], 100, replace = TRUE)), # Nominal categorical variable
ord_var = factor(sample(c("low", "medium", "high"), 100, replace = TRUE),
levels = c("low", "medium", "high"),
ordered = TRUE), # Ordinal variable
cont_var1 = rnorm(100), # Continuous variable 1
cont_var2 = runif(100) # Continuous variable 2
)
# Perform Mixed-Type Clustering using the Deterministic variant
result_mix <- DIBmix(X = data, ncl = 3, catcols = 1:2, contcols = 3:4)
cat("Mixed-Type Clustering Results:\n")
print(result_mix$Cluster)
print(result_mix$Entropy)
print(result_mix$MutualInfo)
# Example Continuous Data
X_cont <- matrix(rnorm(1000), ncol = 5) # 200 observations, 5 features
# Perform Continuous Data Clustering
result_cont <- DIBcont(X = X_cont, ncl = 3, s = -1, nstart = 50)
cat("Continuous Clustering Results:\n")
print(result_cont$Cluster)
print(result_cont$Entropy)
print(result_cont$MutualInfo)
# Example Categorical Data
X_cat <- data.frame(
Var1 = factor(sample(letters[1:3], 200, replace = TRUE)), # Nominal variable
Var2 = factor(sample(letters[4:6], 200, replace = TRUE)), # Nominal variable
Var3 = factor(sample(c("low", "medium", "high"), 200, replace = TRUE),
levels = c("low", "medium", "high"), ordered = TRUE) # Ordinal variable
)
# Perform Categorical Data Clustering
result_cat <- DIBcat(X = X_cat, ncl = 3, lambda = -1, nstart = 50)
cat("Categorical Clustering Results:\n")
print(result_cat$Cluster)
print(result_cat$Entropy)
print(result_cat$MutualInfo)Contributions are welcome! If you encounter issues, have suggestions, or would like to enhance the package, please feel free to submit an issue or a pull request on the GitHub repository.
This package is distributed under the GPL-3 License. See the GNU General Public License version 3 for details.