Abstract
Solving inductive queries which have to return complete collections of
patterns satisfying a given predicate has been studied extensively the
last few years. The specific problem of frequent set mining from
potentially huge boolean matrices has given rise to tens of efficient
solvers. Frequent sets are indeed useful for many data mining tasks,
including the popular association rule mining task but also feature
construction, association-based classification, clustering, etc. The
research in this area has been boosted by the fascinating concept of
condensed representations w.r.t. frequency queries. Such
representations can be used to support the discovery of every frequent
set and its support without looking back at the data. Interestingly,
the size of condensed representations can be several orders of
magnitude smaller than the size of frequent set collections. Most of
the proposals concern exact representations while it is also possible
to consider approximated ones, i.e., to trade computational complexity
with a bounded approximation on the computed support values. This
paper surveys the core concepts used in the recent works on condensed
representation for frequent sets. 1
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