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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)