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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.
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)
Rate this tutorial
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\
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