Cluster Analysis is a technique of grouping of obsevations owning same characteristic into one cluster. There are some types cluster analysis:

*K-Mean cluster*

In K-Mean cluster, number of cluster have been determined or known. Variable which is used in K-Mean cluster is numerical variable. To do it using SPSS, follow these steps :

- click on the main menu : ' analyze -> ' statistics descriptive'
- put variable into ' variables'.
- activating 'save standardized values as variables'.
- click ' analyze -> ' classify -> ' cluster means k' .
- put variable into ' variables' -> fill the number of cluster that you want into 'number of cluster'.
- click ' save -> activating 'cluster membership' and 'distance from cluster center' .
- click ' option', activating 'anova table' -> click ' ok'.

*Hierarchical Cluster*

Hierarchical Cluster is used when the number of cluster is unknown and thenomber of observation at the most 200. Variable which is used in hierarchical cluster is numerical variable. To do it using SPSS, follow these steps :

- click ' analyze -> ' classify -> ' hierarchical cluster' .
- put variable into ' variables'.
- click ' statistics’ , choose 'agglomeration schedule' and fill 'range of solution' .
- click ' plot’ -> activating ' dendogram'.
- click ' method’ , in 'cluster method's’ choose 'ward's method'.
- in ' standardize' choose z score ->click continue -> click ' O.K.'