matzewolf/kMeans

Version 1.1.0.0 (171 KB) by matzewolf
k-means (unsupervised learning/clustering) algorithm implemented in MATLAB.
3.0
(2)
909 Downloads
Updated 13 Jan 2018
View License on GitHub

Cluster_2D_Visualization.m is a script that generates random (uniformly) distributed data points, runs both kMeans.m and MATLAB's built-in kmeans function, measures and compares their performance (i.e. computing time) and visualizes the final clusters and the distribution of the data points in the clusters in a histogram.
kMeans.m implements k-means (unsupervised learning/clustering algorithm). Technical Details:
The initial centroids are randomly selected out of the set of all data points (every data points maximum once).
The stopping condition is that no changes to any cluster is made.
clustering_app.mlapp opens an App with GUI where you can randomly generate data points and cluster them. You can re-hit all buttons to see the randomness in both point generation and the clustering algorithm.
clustering_app.mlappinstall installs the MATLAB App in the MATLAB Editor.

Cite As

matzewolf (2024). matzewolf/kMeans (https://github.com/matzewolf/kMeans), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2017a
Compatible with any release
Platform Compatibility
Windows macOS Linux
Categories
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Version Published Release Notes
1.1.0.0

Added MATLAB GUI App for interactive clustering.
1.0.0.0

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.