K-Means Clustering in Java

Project Overview

This Java project implements the K-means clustering algorithm for categorizing 2-D data based on their positions on a Cartesian plane. Users can specify the number of categories (clusters) for classification. The project includes several key components:

• KMeans.java: Contains the main function.
• Point.java: Defines a point in 2D space.
• Cluster.java: Represents a cluster of points in close proximity.
• CSVHelper: Assists in parsing CSV files to create a 2D array of points.
• Sample CSV files: Provided as sample data.

Technical Requirements

• Java: Compatible with the most recent version of Java.
• External Libraries: No external libraries required.

Usage

Run KMeans.java to start the clustering process. The user can specify the number of clusters. Sample CSV files can be used to test the functionality.