Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm.

It is a density-based clustering algorithm: given a set of points in some space, it groups together points that are closely packed together (points with many nearby neighbors), marking as outliers points that lie alone in low-density regions (whose nearest neighbors are too far away). DBSCAN is one of the most common clustering algorithms and also most cited in scientific literature.

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Requirements: Java 1.8+

Run following command in terminal:

javac DbscanGui.java

Run following command in terminal:

• n is the number of points, you should replace it with a specific number
• k is the number of clusters, you should replace it with a specific number
• min_points is the number of points that are found within the ε-neighborhood
• eps is the value of ε

Run Terminal Version

java DbscanGui n minPoints eps

Run GUI Version

java AgnesGui k minPoints eps