Understanding Clustering Algorithms for Data Analysis in R

Introduction to Cluster Analysis

Cluster analysis, also known as clustering algorithm, is a type of unsupervised machine learning technique that groups similar observations into clusters based on their similarity in features. In this article, we will explore how to apply cluster analysis to your database in R.

Background and Motivation

Cluster analysis is widely used in various fields such as marketing, customer behavior, medical research, and data mining. It helps identify patterns or structures in the data that are not readily apparent through other methods of data analysis.

In this example, we have a dataset containing information about applicants to a course, including their notes (a sum of the row multiplied by 2). The goal is to group these applicants into categories based on their notes using cluster analysis. We will explore different clustering algorithms and techniques that can be applied to your database in R.

Installing Required Packages

Before we begin, make sure you have installed the required packages in R:

install.packages("factoextra")
install.packages("fviz")

These packages provide functions for data visualization and clustering analysis.

Data Preparation

To apply cluster analysis to your dataset, you need to prepare it for analysis. The first step is to ensure that the data is properly formatted and cleaned.

In this example, we have a dataset containing information about applicants to a course, including their notes (a sum of the row multiplied by 2). We can create a new dataframe with the necessary columns:

example <- data.frame(
  candidate = aspirante,
  sector = sector,
  p1 = question1,
  p2 = question2,
  p3 = question3,
  p4 = question4,
  p5 = question5,
  note = note
)

Choosing Clustering Algorithms

There are several clustering algorithms that can be applied to your dataset, including:

K-Means: This algorithm is widely used for clustering data. It partitions the data into k clusters based on their similarity.
Hierarchical Clustering: This algorithm builds a hierarchy of clusters by merging or splitting existing clusters.
DBSCAN (Density-Based Spatial Clustering of Applications with Noise): This algorithm groups data points into clusters based on their density and proximity to each other.

Applying K-Means Algorithm

We can apply the K-Means algorithm to our dataset using the following R code:

library(factoextra)

# Apply K-Means algorithm
kmeans <- kmeans(example[,-ncol(example)], k = 4, nstart = 10)

In this code, example[,-ncol(example)] refers to all columns except the note column. The k parameter specifies the number of clusters (in this case, 4). The nstart parameter specifies the number of times to run the algorithm for better results.

Visualizing Cluster Assignments

We can visualize the cluster assignments using a 2D scatter plot:

fviz_cluster(kmeans)

This code generates a 2D scatter plot showing the cluster assignments.

Applying Hierarchical Clustering Algorithm

We can apply the Hierarchical Clustering algorithm to our dataset using the following R code:

library(factoextra)

# Apply Hierarchical Clustering algorithm
hclust <- hclust(dist(example[,-ncol(example)]), method = "ward.D2")

In this code, dist(example[,-ncol(example)]) calculates the pairwise distances between data points. The method parameter specifies the linkage method (in this case, Ward’s Minimum Variance).

Visualizing Hierarchical Clustering Tree

We can visualize the hierarchical clustering tree using a dendrogram:

fviz_dendrogram(hclust)

This code generates a dendrogram showing the hierarchical clustering tree.

Applying DBSCAN Algorithm

We can apply the DBSCAN algorithm to our dataset using the following R code:

library(factoextra)

# Apply DBSCAN algorithm
dbscan <- dbscan(example[,-ncol(example)], eps = 1.5, metric = "euclidean")

In this code, eps specifies the epsilon value for clustering, and metric specifies the distance metric (in this case, Euclidean).

Visualizing DBSCAN Clusters

We can visualize the DBSCAN clusters using a 2D scatter plot:

fviz_cluster(dbscan)

This code generates a 2D scatter plot showing the DBSCAN clusters.

Conclusion

In conclusion, cluster analysis is a useful technique for identifying patterns or structures in data. We have explored different clustering algorithms (K-Means, Hierarchical Clustering, and DBSCAN) and techniques that can be applied to your database in R. By applying these algorithms and visualizing the results, we can gain insights into the underlying structure of the data.

Note: The note column is not used for clustering analysis because it’s a multiple value per row which doesn’t fit well with the traditional clustering algorithm.

Last modified on 2023-09-23