Rough Sets Analysis (RSA) is the method to understand the phenomena roughly. It is also a method to find characteristics.
It is one of the methods of Data Mining .
The logic of "Rough sets" in mathematics and the method to find rough sets are not a pair. The method to find rough sets is studied in Sensitivity Engineering for design and planning. In this page, "Rough sets analysis" (RSA) is the method used in sensitivity engineering.
The Y of rough sets is category data, for example, "Go" and "No-Go". Here, X is also category data. But quantity data can be used.
The quantity data used in RSA is from 0 to 1. The variable expresses the pair idea like "Yes and No" and "Hot and Cold". SD method uses such data. So RSA is useful for the analysis questionnaire .
If the lower approximation of "Go" is "A=1 and B=2", it does not mean the lower approximation of "No-Go" is "A=2 and B=1". It is interesting.
There are similar methods of RSA. Relationship to the associations analysis is wrote in Associations Analysis .
We can also find the part of rough sets (lower and upper approximations) by Decision Tree .
We cannot find all part of rough sets because decision tree uses a tree model. But decision tree helps RSA because RSA is not good at big size data. Decision tree is expected to find main part of rough sets.
Discriminant analysis also tells us the relationship between Y and X.
RSA is useful to analyze complicated interaction structure.