What is the difference between a decision tree and something called "subgroup discovery algorithms"?

I'm reading a paper which states that subgroup discovery is:

Subgroup discovery is a data mining technique whose goal is to detect interesting subgroups into a
population with respect to a property of interest 

The paper goes on to make the distinctions between a decision tree and subgroup discovery, but does not explain the rationale/reasoning.

With a google search on subgroup discovery algorithms I find the following:

The goal of the subgroup discovery algorithm SD, outlined in Figure 1, is to search for rules that 
maximize $q_{g} =
\frac{TP}{FP + g}$, where TP are true positives, FP are false positives, and g is a generalization 
parameter. High quality rules cover many target class examples and a low number of non-target e 
xamples

and decision tree according to Wikipedia:

A tree is built by splitting the source set, constituting the root node of the tree, into subsets— 
which constitute the successor children. The splitting is based on a set of splitting rules based on 
classification features.[2] This process is repeated on each derived subset in a recursive manner 
called recursive partitioning.

This + my prior knowledge of decision trees is not able to make any distinctions between the two. Is a decision tree a part of "Subgroup discovery algorithms" or not?