How to use SPSS for cluster analysis

Clustering can be performed through [Clustering] and [Hierarchical Clustering] in the SPSSAU Advanced Methods module. Combined with the help manual, it can be easily interpreted.

Three clustering methods.

Hierarchical clustering, kmean clustering, and two-stage clustering have different requirements for data, depending on which one to use for your data.

The steps are as follows:

Operating Equipment: Dell Computer.

Operating system: win10

1. First through the shortcut.

Open the SPSS analysis tool, and the data view is displayed by default.

2. Switch to the variable view, and then add six variables, namely name, m, c, e, s, and r, where the name is a string.

type, everything else is a numeric type.

3. Return to the data view and insert the corresponding data into the six variable columns.

4. Click the Analysis menu, and then select Classification --- Systematic Clustering.

5. Open the system clustering analysis window and move the variables m and c to the variable box.

6. Click the statistics button on the right to open the system cluster analysis: statistics window, select the ridge concentration plan, and then click Continue.

7. Click the graph button to open the graph settings window, check the pedigree chart, and then click Continue.

8. Then click the method button to open the system clustering analysis: destruction band method window, select Wald's method as the clustering method, and then click Continue.

9. Finally, click the OK button in the system clustering analysis window, and then generate the system clustering analysis results and graphical display.

spssCluster analysisThe polymerization coefficient is used to determine the classification into 2 categories.

The number of classes clustered by the system can be determined using the aggregation coefficient. For the SPSS operation of system clustering, please refer to the following experience items, which will not be repeated here. In the output result of SPSS system clustering, the coefficient column of the Clustering Table is the aggregation coefficient.

Copy the clustering table into Excel, utilize the total number of samples participating in clustering, and subtract the first column in the clustering table, which will be divided into the number of categories. In this example, there are 17 samples of slim friends participating in the forest rent clustering, so the "Number of Categories" column is equivalent to subtracting the value of the first column from 17 respectively.

Cluster analysis

Refers to the analytical process of grouping a collection of physical or abstract objects into classes made up of similar objects. It is an important omen of human behavior. The goal of cluster analysis is to collect data for classification on a similar basis.

Clustering stems from many fields, including mathematics and computer science.

Statistics, Biology and Economics. A number of clustering techniques have been developed in different application areas, and these techniques have been used to describe data, measure similarity between different data sources, and classify data sources into different clusters.