K-Mean Clustering Tutorials

By Kardi Teknomo, PhD.

This tutorial will introduce you to the heart of Pattern Recognition, unsupervised learning of Neural network called k-means clutering. When User click picture box to input new data (X,Y), the program will make group/cluster the data by minimizing the sum of squares of distances between data and the corresponding cluster centroids. Different color code represent the clusters. This algorithm is a standard and popular algorithm for unsupervised learning of Neural network, Pattern recognitions, Classification analysis, clustering analysis etc.

What is K-Mean Clustering?

Numerical Example (hand calculation); Spanish translation available here

How the K-Mean Clustering algorithm works?

Download Code in VB (Screenshot)

Download code in Matlab

What is the mimimum number of attributes?

What are the applications of K-mean clustering?

What are the weaknesses of K-Mean Clustering?

Are there any other resources for K-mean Clustering?

Citation (other papers that has reference to this tutorial)

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See also: Similarity and Dissmilarity Measurements (for multivariate distances) Discriminant Analysis (LDA), K Nearest Neighbor

This tutorial is copyrighted.

Preferable reference for this tutorial is

Teknomo, Kardi. K-Means Clustering Tutorials. http:\\people.revoledu.com\kardi\ tutorial\kMean\