DBSCAN Clustering Plotting through ggplot2

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a popular clustering algorithm used to group data points into clusters based on their density and proximity to each other. In this article, we will explore how to visualize the DBSCAN clustering result using the ggplot2 package in R.

Overview of DBSCAN

DBSCAN works by identifying clusters as follows:

A point is considered a core point if it has at least minPts number of points within a distance of eps.
All points within a certain distance of a core point are considered part of the same cluster.
Points that do not meet these conditions are considered noise.

DBSCAN can be sensitive to outliers and is often used in combination with other algorithms to improve its performance.

Plotting DBSCAN Clustering using ggplot2

The original R code provided in the Stack Overflow question attempts to plot the DBSCAN clustering result using ggplot2. However, there are some issues with this approach that need to be addressed:

Ignoring noise points: When plotting the cluster assignment using res$cluster, it ignores points with 0 labels (noise points).
Incorrect color mapping: Using res$cluster+1 as the color mapping can lead to incorrect results, as it maps 0 to 1 and thus incorrectly colors noise points.

A better approach is to subset the data into clusters and then plot each cluster separately using ggplot2.

Step 1: Subsetting Data

First, we need to create a new column in our original data that represents the cluster assignment. We can do this by taking the third column from the DBSCAN output and assigning it as a factor:

data <- dataframe with the cluster column (still in numeric form).

Step 2: Filtering Noise Points

Next, we need to filter out noise points by only considering data points with cluster assignments greater than 0:

data2 <- dplyr::filter(data, cluster > 0)

This will create a new data frame data2 that contains only the non-noise points.

Step 3: Plotting Clusters using ggplot2

Now we can use ggplot2 to plot each cluster separately. We’ll use the cluster assignment as the color mapping and assign a shape based on the cluster label:

ggplot(data2, aes(x = x, y = y) +
    geom_point(aes(color = `cluster`)))

This will produce a plot where each cluster is represented by a different color.

Adding Noise Points to the Plot

To add noise points to the plot with a specific symbol and color (in this case, grey), we can use the geom_path function in combination with the data.frame function:

noisy_data <- data.frame(x = data2$x, y = data2$y, cluster = 0)
ggplot(data2, aes(x = x, y = y) +
    geom_point(aes(color = `cluster`) +
    geom_path(data = noisy_data, aes(x = x, y = y), color = "grey", linetype = 2))

This will add the noise points to the plot as a grey line.

Conclusion

In this article, we explored how to visualize DBSCAN clustering results using ggplot2 in R. We discussed some common challenges when working with DBSCAN and provided an approach for plotting clusters using ggplot2 that avoids ignoring noise points and incorrectly maps color assignments.

Last modified on 2024-10-21