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.

Tired of ads? Do want to read comfortably this tutorial from any device? Purchase the complete e-book of this k means clustering tutorial.

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)

See also:

Similarity and Dissmilarity Measurements (for multivariate distances)
Discriminant Analysis (LDA)
K Nearest Neighbor

Read this tutorial off-line in any device. Purchase the complete e-book of K Means Clustering tutorial here

This tutorial is copyrighted.

Preferable reference for this tutorial is

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