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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.

Topics of this k means tutorials:

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)