K-Means Cluster Analysis

This procedure attempts to identify relatively homogeneous groups of cases based on selected characteristics, using an algorithm that can handle large numbers of cases. However, the algorithm requires you to specify the number of clusters. You can specify initial cluster centers if you know this information. You can select one of two methods for classifying cases, either updating cluster centers iteratively or classifying only. You can save cluster membership, distance information, and final cluster centers. Optionally, you can specify a variable whose values are used to label casewise output. You can also request analysis of variance F statistics. While these statistics are opportunistic (the procedure tries to form groups that do differ), the relative size of the statistics provides information about each variable's contribution to the separation of the groups.

Example. What are some identifiable groups of television shows that attract similar audiences within each group? With k-means cluster analysis, you could cluster television shows (cases) into k homogeneous groups based on viewer characteristics. This process can be used to identify segments for marketing. Or you can cluster cities (cases) into homogeneous groups so that comparable cities can be selected to test various marketing strategies.

Statistics. Complete solution: initial cluster centers, ANOVA table. Each case: cluster information, distance from cluster center.

Show me

K-Means Cluster Analysis Data Considerations

Data. Variables should be quantitative at the interval or ratio level. If your variables are binary or counts, use the Hierarchical Cluster Analysis procedure.

Case and initial cluster center order. The default algorithm for choosing initial cluster centers is not invariant to case ordering. The Use running means option in the Iterate dialog box makes the resulting solution potentially dependent on case order, regardless of how initial cluster centers are chosen. If you are using either of these methods, you may want to obtain several different solutions with cases sorted in different random orders to verify the stability of a given solution. Specifying initial cluster centers and not using the Use running means option will avoid issues related to case order. However, ordering of the initial cluster centers may affect the solution if there are tied distances from cases to cluster centers. To assess the stability of a given solution, you can compare results from analyses with different permutations of the initial center values.

Assumptions. Distances are computed using simple Euclidean distance. If you want to use another distance or similarity measure, use the Hierarchical Cluster Analysis procedure. Scaling of variables is an important consideration. If your variables are measured on different scales (for example, one variable is expressed in dollars and another variable is expressed in years), your results may be misleading. In such cases, you should consider standardizing your variables before you perform the k-means cluster analysis (this task can be done in the Descriptives procedure). The procedure assumes that you have selected the appropriate number of clusters and that you have included all relevant variables. If you have chosen an inappropriate number of clusters or omitted important variables, your results may be misleading.

To Obtain a K-Means Cluster Analysis

This feature requires the Statistics Base option.

1. From the menus choose:

   Analyze > Classify > K-Means Cluster...

2. Select the variables to be used in the cluster analysis.
3. Specify the number of clusters. (The number of clusters must be at least 2 and must not be greater than the number of cases in the data file.)
4. Select either Iterate and classify or Classify only.
5. Optionally, select an identification variable to label cases.