Clustering 5 dimensions into 4 based on mean? In my project, one variable named Organizational Culture has 5 dimensions namely Employee Development, Harmony, Customer Orientation, Social Responsibility and Innovation. In SPSS, I need to configure these 5 dimensions into 4 types so that:


*

*The highly integrative culture has high scores on all 5 dimensions.

*The market oriented culture describes the organizations that emphasize customer orientation more than any other cultural dimension.

*The moderately integrative culture shows average scores on all ?ve culture dimensions.

*Lastly, hierarchy culture show low score on all culture dimensions.


In short, here is what I need:
I need to cluster the firms from which I got data into 4 types (highly integrative, moderately integrative and so on) on the basis of the score assigned by their (firms') managers to the the 5 culture dimensions. As a result I will have four types of firms which will have four different culture types. I do see some hope in the method you suggest in your above post. Thank you very much for your time and I appreciate any help :)
 A: Nice question, and nice dataset (+1)
Here you have to take decisions. You know where your clusters are intuitively since you are able to describe them. Cluster analysis, or Principal Component Analysis etc. would give you the "natural" segmentation of your dataset, not necessarily the one that you want to impose on it.
As I see it, you have to make the jump from "high score on all dimensions" to 5 defined numbers. You can make 4 centroids that correspond to the average 5 scores you expect from each type and assign each company to the type of the nearest centroid (Euclidean distance).
An alternative might be to use a trusted subset of the data where you know which class a company belongs to in order to train a model like Linear Discrimnant Analysis. Once the model is trained, you can use it to predict the class of the other companies.
In R this is really easy.
library(MASS)
trained <- lda(training_set, known_types)
types <- predict(trained, dataset)$class

A: Not sure, but I think the centroid-based suggestion by Gui11aume assumes that you are going to include each and every case in one of the clusters.  In practice I often find that, when one sets up criteria such as yours to define the clusters, only perhaps 50% to 80% of cases will make a good fit to any of these descriptions. (Why should a priori profiles apply to everyone?) So if it is acceptable to you and/or consistent with your knowledge of the situation to have some cases left out, consider using a series of DO IF and ELSE IF statements to assign some cases to clusters, something like the following.
*For those high on all 5 dimensions.
DO IF EmployeeDevelopment > [threshold] and Harmony > [threshold] and CustomerOrientation > [threshold] and SocialResponsibility > [threshold] and Innovation > [threshold].
Compute Cluster = 1.
*For those high only on Customer Orientation.
ELSE IF EmployeeDevelopment < [threshold] and Harmony < [threshold] and CustomerOrientation > [threshold] and SocialResponsibility < [threshold] and Innovation < [threshold].
Compute Cluster = 2.
*Etc. for the other 2 clusters.
END IF.
EXE. 
You may need to adjust the structure of this syntax a little.  If it gives you problems, you can simplify using the following.  It's just that on a very large data set it will require more computing time. 
IF [cluster 1 criteria are met] cluster = 1.
IF [cluster 2 criteria are met] cluster = 2.
*Etc.
EXE.
With this framework, whatever command you include last will overwrite the earlier ones, so you'll need to be conscious of the order you choose.
