K-means

K-means is the most famous clustering algorithm. In this tutorial we review just what it is that clustering is trying to achieve, and we show the detailed reason that the k-means approach is cleverly optimizing something very meaningful.

K-Means Clustering Overview
Overview
K-Means clustering generates a specific number of disjoint, flat (non-hierarchical) clusters. It is well suited to generating globular clusters.

k-means Clustering
Data Mining with the k-means Algorithm
By Mike Chapple, About.com Guide ...

K-means clustering
One of the most popular clustering techniques.
Given an integer K, K-means partitions the data set into K non overlapping clusters.

K-Means Clustering Code Example
By Kardi Teknomo, PhD.
As an example, I have made a Visual Basic code. You may download the complete code plus this tutorial in here or at the mirror.

k-means clustering and EM clustering on an artificial dataset ("mouse"). The tendency of k-means to produce equi-sized clusters leads to bad results, while EM benefits from the Gaussian distribution present in the data set ...

K-Means Clustering
The Algorithm
K-means (MacQueen, 1967) is one of the simplest unsupervised learning algorithms that solve the well known clustering problem.

K-means A parametric algorithm for clustering data into exactly k clusters. First, define some initial cluster parameters. Second, assign data points to clusters. Third, recompute better cluster parameters, given the data assignment.

k-Means Clustering: -
In k-means clustering, the clustering procedure begins with a single cluster that is successively split into two clusters. This continues till the required number of clusters is obtained.

k-means clustering. To reiterate, the classic k-Means algorithm was popularized and refined by Hartigan (1975; see also Hartigan and Wong, 1978).

5 K-Means Clustering 242
5.6 Recursive Least-Squares Estimation of the Weight Vector 245
5.7 Hybrid Learning Procedure for RBF Networks 249
5.8 Computer Experiment: Pattern Classification 250
5.9 Interpretations of the Gaussian Hidden Units 252
5.

And they include other data mining operations such as clustering (mixture models, k-means and hierarchical), Bayesian networks and Reinforcement Learning.

See also: Clustering, K-means clustering, Classification, Data mining, Regression