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K-means clustering

version 1.0.0.0 (3.3 KB) by Reza Ahmadzadeh
Simple implementation of the K-means algorithm for educational purposes

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Updated 20 Jan 2018

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This is a simple implementation of the K-means algorithm for educational purposes. k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.

Comments and Ratings (2)

'mvnrnd' requires Statistics and Machine Learning Toolbox.

MATLAB Release Compatibility
Created with R2016b
Compatible with any release
Platform Compatibility
Windows macOS Linux