This article describes how to do a k-means cluster analysis in Displayr. The k-Means cluster analysis algorithm is a method for grouping similar cases into groups, or clusters. The final clusters will be different from each other, while the cases within a cluster are broadly similar to each other.
- A data set containing the variables that you want to use as inputs to the cluster analysis segmentation.
- Familiarity with the Structure and Value Attributes of Variable Sets.
1. Login into Displayr and load a document.
2. Load the data set that you contains the variables that you want to use as inputs to the cluster analysis.
3. From the toolbar menu, select Anything > Advanced Analysis > Cluster > K-Means Cluster Analysis. A cluster analysis object will added to the current page.
4. From the object inspector on the right, select the inputs (clustering variables) from the Variables drop-down in the Inputs section. For this example, we'll select the 11 behavioral/attitudinal statements on mobile technology. Questions were asked as a 5-point agree/disagree scale. We'll use the top 2 box responses to each of the statements as the inputs to our k-Means cluster analysis.
You can use any other numeric variables as clustering variables that can potentially provide differentiation between the respondents and therefore help define the clusters.
Note that if the variables are grouped in a Variable Set, then the Variable Set may be selected instead, which is more convenient than selecting multiple variables.
5. Select the number of clusters that you want to create in the Number of clusters selection text box. I've selected 3 clusters for this example, but you can choose any value you want here.
6. Optional: Modify any of the other input settings as desired. For this example, we'll leave the default values selected.
7. Click the Calculate button (or tick the Automatic checkbox so that the analysis will re-run automatically if any changes are made).
The following output is generated: