K Means Cluster

Group Observations Using Distances

Use the K Means Cluster platform to group observations that share similar values across a number of variables. Use the k-means method with larger data tables, ranging from approximately 200 to 100,000 observations.

The K Means Cluster platform constructs a specified number of clusters using an iterative algorithm that partitions the observations. The method, called k-means, partitions observations into clusters so as to minimize distances to cluster centroids. You must specify the number of clusters, k, in advance. However, you can compare the results of different values of k to select an optimal number of clusters for your data.

Figure 13.1 3D Biplot 

Contents

Overview of the K Means Cluster Platform

Overview of Platforms for Clustering Observations

Example of K Means Cluster

Launch the K Means Cluster Platform

Iterative Clustering Report

Iterative Clustering Options

Iterative Clustering Control Panel

K Means Report

Cluster Comparison Report

K Means Report

K Means Report Options

Self Organizing Map

Self Organizing Map Control Panel

Self Organizing Map Report

Description of SOM Algorithm

Additional Example of K Means Cluster Platform

Example of a Self-Organizing Map