Exploring data attributes I have a database with many attributes. I would like to know which attributes has the minimum variation in the data. Is there some standard technique? It should be like clustering without split records in clusters. I would like to know what the records in particular cluster have in common. 
I was going to compute the mean ($\bar{x}$) and st.d. ($s$) for each continuous attribute $x$. After computing the coefficient of variation $CV=\frac{s}{\bar{x}}$ I would say that attributes with $CV\leq0.1$ are the similar ones. For categorical ones I would choose attributes with more than $90\%$ relative frequency for the mode.
Is there some standard technique?
 A: It reminds me of what is implemented in the caret package for data pre-processing. It is fully described in one of the accompanying vignette, namely Data Sets and Miscellaneous Functions in the caret Package. What is actually done is to identify predictors that have low variance in the full dataset, as you described, whether it be a continuous or a categorical feature. They compute:


*

*the frequency of the most prevalent value over the second most frequent value (termed "frequency ratio"),

*the proportion of unique values (subject-wise),


considering that

If the frequency ratio is less than a
  pre–specified threshold and the unique
  value percentage is less than a
  threshold, we might consider a
  predictor to be near zero–variance. (p. 5, emphasis is mine)

The rationale is that near-zero variance predictors may have exact zero variance when using cross-validation, or induce model instability. They also address the problem of collinearity, but then this really is a matter of statistical modeling (some models, like classical regression models, don't accommodate well correlated predictors because it will inflates standard error of regression coefficients; others don't care about that).
Besides screening those low informative predictors, you can also use a hierarchical clustering method (by variables, not by individuals) to see how it goes. This is often used for studying missing data patterns (i.e., where we are interested in examining which variables are consistently showing an increased number of missing responses across all samples, or a particular subgroup).
