If a high quality subgroup is present, it is very likely that several variants of this
subgroup exist, which are also evaluated as good subgroups (e.g., by adding a constraint
A 	= v for some attribute A and a rare feature v).Furthermore, deviation analysis typically focusses on a
target variable rather than associations between any attributes, which offers some
optimization potential [6].If it is possible to transform the database such that association mining
(e.g., via the Apriori algorithm) can be applied, the validation and ranking of
the patterns found are merely a post-processing step [15].Thus, it may happen that
198 7 Finding Patterns
many of the subgroups in the beam are variations of a single scheme, which prevents
the beam search from focussing on other parts of the dataspace--a small number of
diverse subgroups would be preferable.A subset of diverse subsets can be extracted
from the beam by selecting successively those subgroups that cover most of the
data.Exhaustive
searching is prohibitive unless intelligent pruning techniques are applied
that prevent us from losing to much time with redundant, uninteresting subgroups.For nominal target variables, efficient algorithms from association rule mining
can be utilized.If most of the subgroups in the beam are variations of
one core subgroup, only a few diversive subgroups will be selected.Thereby subgroups cannot be rediscovered, but the method has to focus on different
parts of the dataspace. 8).