A new class of rules, called action rules, show what actions should be taken to improve the profitability of customers. Action rules assume that attributes in a database are divided into two groups: stable and flexible. These reflect the ability of a business user to influence and control their change for a given consumer. In this paper, we introduce a new classification of attributes partitioning them into stable, semi-stable, and flexible. Values of stable attributes can not be changed for a given consumer (for instance “maiden name” is an example of such an attribute). So, stable attributes have only one interpretation. If values of an attribute change in a deterministic way as a function of time (for instance values of the attribute “age”), we call them semi-stable. All remaining attributes are called flexible. In the process of action rule extraction, stable attributes are highly undesirable. What about semi-stable attributes? Although, they seem to be quite similar to stable attributes, the difference between them is quite essential. Semi-stable attribute may have many different interpretations but among them only one interpretation is natural and it is called standard. All its other interpretations are called non-standard. Strategy based on distributed knowledge mining is used in this paper as a tool to identify which semi-stable attributes have non-standard interpretation so they can be classified as flexible. This way, by decreasing the number of stable attributes in a database we may discover action rules which would not be discovered otherwise.