The MATLAB code described in this article implements a complete demonstration of the K-Means clustering algorithm, suitable for data clustering learning and algorithm validation. The code intuitively demonstrates the working principle and performance of K-Means through simulated data generation, clustering analysis, result visualization, and error assessment.
Table of Contents
- Program Introduction
- Core Functions of the Code
- Key Features
- Running Results
- MATLAB Code
- Program Structure
- Partial Code
- Full Code Access
Program Introduction
Core Functions of the Code
-
Data Generation
- Create 4 Gaussian distribution clusters (a total of 200 sample points)
- Each cluster has different center points
<span>[3,3]</span>,<span>[-3,3]</span>,<span>[3,-3]</span>,<span>[-3,-3]</span>and standard deviations - The data is merged and then randomly shuffled to simulate a real dataset
K-Means Clustering
<span>k=4</span>(specifying the number of clusters)<span>MaxIter=100</span>(maximum number of iterations)<span>Replicates=5</span>(repeated clustering to obtain the optimal solution)
- Use MATLAB’s built-in
<span>kmeans()</span>function to perform clustering - Key parameters:
- Output clustering labels, centroid coordinates, and within-cluster errors
Visualization Analysis
- Original data distribution (same color)
- True label distribution (distinguished by different symbols)
- Comparison chart of clustering results and true labels
- Clustering Result Chart: Scatter points colored to distinguish clusters, black