cluster ordinal data

Question

I want to do clustering of my data (kmeans or hclust) in R language (coding). My data is ordinal, which means that the data is Likert scale to measure the causes of cost escalation (I have 41 causes "variables") that scaled from 1 to 5, which 1 is no effect to 5 major effect (I have about 160 observations "who rank the causes")... any help of how to cluster the 41 cause based on the observations ... do I have to convert the scale to percentage or z score before clustering or any thing that help ...... I really need your help!! here is the data to play with https://docs.google.com/spreadsheet/ccc?key=0AlrR2eXjV8nXdGtLdlYzVk01cE96Rzg2NzRpbEZjUFE&usp=sharing

I want to cluster the variables (the columns) in terms of similarity of occurrence in observations... I follow the code in statmethods.net/advstats/cluster.html; but I couldn't cluster the variables (the columns) in terms of similarity of occurrence in observations and also I follow the work at mattpeeples.net/kmeans.html#help; but I don't know why he convert the data to percentage and then to Z-score standardize.

Answer 1

It isn't clear to me if you want to cluster the rows (the observations) in terms of similarity in the variables, or cluster the variables (the columns) in terms of similarity of occurrence in observations?

Anyway, see package cluster. This is a recommended package that comes with all R installations.

Read ?daisy for details of what is done with ordinal data. This metric can be used in functions such as agnes (for hierarchical clustering) or pam (for partitioning about medoids, a more robust version of k-means).

By default, these will cluster the rows/observations. Simply transpose the data object using t() if you want to cluster the columns (variables). Although that may well mess up the data depending on how you have stored them.

Answer 2

Converting the data to percentage is called normalization of data so all the variables are in the range of 0 - 1.

If data is not normalized you run the risk of bias towards dimensions with large values