Automatically Detect Valid/Interesting pivot/drill downs of a dataset Imagine you are given a tabular data set with a limited set of columns and rows and you are asked to find the valid/interesting pivot configuration by exploring the data. The brute-force option is to calculate all possible pivot configuration and somehow score the result features (such as its sparsity, count,...) and pick the ones that are scored higher. This is obviously very time-consuming.

I understand that my definition of "Valid/Interesting" is fuzzy in here but is there a more science-based approach (say using correlations, column cardinality,...) to automatically find the good pivot configuration of a given dataset? Any pointer is highly appreciated.
The automated generation of pivot tables is a well-studied topic. In fact, Google has a patent on the technology.

The general approach is:


Identify low-cardinality, categorical data features that have many repetitions. Those features become the row and column candidates for the pivot table.  
Identify numerical data that are the candidates for the data (or fact) cells in the pivot table.
Select an aggregation function (e.g., count, sum, average, …).
Select an evaluation method. The most common evaluation methods are human rating or look-a-like modeling with historical data.


Generating useful pivot tables typically requires domain knowledge.