Choosing a Procedure for Clustering

Cluster analyses can be performed using the TwoStep, Hierarchical, or K-Means Cluster Analysis procedure. 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 continuous variables.
• Ability to save the cluster model to an external XML file and then read that file and update the cluster model using newer data.

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

Hierarchical Cluster Analysis. 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 dissimilarity 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.

K-Means Cluster Analysis. The K-Means Cluster Analysis procedure is limited to continuous data and requires you to specify the number of clusters in advance, but it has the following unique features:

• Ability to save distances from cluster centers for each object.
• Ability to read initial cluster centers from and save final cluster centers to an external IBM® SPSS® Statistics file.

Additionally, the K-Means Cluster Analysis procedure can analyze large data files.