Java-3D-Point-Clustering

📊 DBSCAN Clustering in Java

This project implements the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm in Java.
It clusters 3D points based on density, labels noise points, assigns random colors to clusters, and outputs the results to a CSV file.

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🚀 Features

• Reads 3D points from a .csv file.
• Implements DBSCAN with parameters:
  • eps → maximum distance for neighborhood inclusion.
  • minPts → minimum number of neighbors for a core point.
• Detects and labels clusters and noise points.
• Assigns random RGB colors to each cluster.
• Saves the clustered points with labels and colors to a new .csv.
• Displays cluster sizes (largest to smallest) and noise count.

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🔍 Classes Overview

DBScan.java → main program, clustering logic, CSV input/output.

NearestNeighbors.java → finds neighbors within eps distance.

Point3D.java → represents 3D points with coordinates, labels, and RGB values.

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👤 Author

Othmane Daali