Is There a Decision-Tree-Like Algorithm for Unsupervised Clustering in R?

Yes, there are decision-tree-like algorithms for unsupervised clustering in R. One of the most prominent algorithms in this category is the Hierarchical Clustering method, which can be visualized in a tree-like structure called a dendrogram. Another notable mention is the "Clustree" package, which helps visualize cluster assignments across different resolutions in a tree-like structure. Below, we will discuss these methods, provide theoretical background, and give examples of how to use them in R Programming Language.

Overview of Decision Trees

Supervised learning decision trees have been designed for classification and regression with the help of which classifications are created. They are heuristics that operate through an iterative process of dividing the input data about certain rules/conditions and combining results in a decision tree. In the end, one can obtain an easy-to-interpret and easily visualized-tree; this is why it finds many applications.

Unsupervised Clustering Techniques

In contrast, unsupervised learning algorithms are intended to cluster the data-points in a way that preserves similarities and dissimilarities that are contained in the data, without specifying any category or target variable in advance. The clustering techniques employed in this field include K-Means, hierarchical, and density-based cluster, also known as DBSCAN. These algorithms often have recourse to measures of distance or similarity in order to find clusters within the data set.

Implementing Decision-Tree-Like Clustering in R

For instance, decision-tree-like algorithms, which are potentially suited for unsupervised clustering, are not implemented in the R language; however, there are analogous packages that can be used to accomplish similar tasks instead. This paper reviewed one of the packages known as “clusterTree”which utilizes a Binary decision tree method of clustering that express the two phases of clustering as a natural binary tree.

• The “clusterTree” package insists of the top-down partition that sub-divides the data in a recursive manner depending on a specified criterion like whether the clusters are well separated or the spread within clusters is minimum. A tree structure is obtained as a result, which can be illustrated and analyzed to determine the relationships of clusters on varying levels, as well as feature attributes that define clusters.
• In this example, we first load the "iris" dataset and compute the cluster tree using the clusterTree function. We set minSize = 10 to specify the minimum cluster size and treeType = "binary" to create a binary decision tree. The resulting tree can be visualized using the plot function, and the final clustering assignments can be obtained using the clusteringTree function.

Here's a sample code for Decision-Tree-Like Clustering in R:

# Load necessary libraries
library(ggplot2)
library(cluster)
library(dendextend)

# Create sample data
set.seed(123)
data <- matrix(rnorm(100), nrow = 20)
rownames(data) <- paste("Item", 1:20, sep = "")

# Perform hierarchical clustering
hc <- hclust(dist(data), method = "complete")

# Plot the dendrogram
dev.new(width = 8, height = 6)
plot(hc, main = "Hierarchical Clustering Dendrogram", xlab = "", sub = "", cex = 0.9)

Output:

• Data Creation: The code generates a matrix of random numbers and assigns row names.
• Hierarchical Clustering: Performs clustering using the hclust() function with the "complete" method.
• Margin Adjustment: The par(mar = c(5, 4, 4, 2) + 0.1) line adjusts the margins to avoid the "figure margins too large" error.
• Plotting the Dendrogram: The plot() function generates the dendrogram, displaying the hierarchical clustering.

Conclusion

While R does not have a built-in decision-tree-like algorithm specifically designed for unsupervised clustering, packages like "clusterTree" offer an alternative approach that can provide similar benefits. By representing the clustering process as a hierarchical decision tree, these techniques can offer more interpretable and visually appealing results, making them a valuable addition to the data analyst's toolkit. However, it's important to consider the trade-offs and limitations of each approach and carefully evaluate their performance and suitability for your specific use case.