User Guide

Chapter

ChoosingaPr

ocedure for Clustering

Cluster analyses can be performed using the TwoStep, Hierarchical, or K-Means

Cluster Ana

lysis procedures. Each procedure employs a different algorithm for

creating clusters, and each has options not available in the others.

TwoStep Cluster Analysis. For many applications, the TwoStep Cluster Analysis

procedure will be the method of choice. It provides the following unique features:

 Automatic selection of the best number of clusters, in addition to measures

for choosing between cluster models.

 Ability to create cluster models simultaneously based on categorical and

continuou

svariables.

 Ability t

o save the cluster model to an external XML file, then read that file and

update the cluster model using newer data.

Additiona

lly, the TwoStep Cluster Analysis procedure can analyze large data files.

Hierarchi

cal Cluster A nalysis.

The Hierarchical Cluster Analysis procedure is limited

to smaller data files (hundreds of objects to be clustered) but has the following

unique features:

 Ability to cluster cases or variables.

 Ability to compute a range of possible solutions and save cluster memberships

for each of those solutions.

 Several methods for cluster formation, variable transformation, and measuring the

dissimil

arity between clusters.

As long as all the variables are of the same type, the Hierarchical Cluster Analysis

procedure can analyze interval (continuous), count, or binary variables.

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