  In this paper we present the GFP-growth (Guided FP-growth) algorithm, a novel
method for multitude-targeted mining: finding the count of a given large list
of itemsets in large data. The GFP-growth algorithm is designed to focus on the
specific multitude itemsets of interest and optimizes the time and memory
costs. We prove that the GFP-growth algorithm yields the exact frequency-counts
for the required itemsets. We show that for a number of different problems, a
solution can be devised which takes advantage of the efficient implementation
of multitude-targeted mining for boosting the performance. In particular, we
study in detail the problem of generating the minority-class rules from
imbalanced data, a scenario that appears in many real-life domains such as
medical applications, failure prediction, network and cyber security, and
maintenance. We develop the Minority-Report Algorithm that uses the GFP-growth
for boosting performance. We prove some theoretical properties of the
Minority-Report Algorithm and demonstrate its performance gain using
simulations and real data.
