A Guided FP-growth algorithm for fast mining of frequent itemsets from big data

In this paper we present the GFP-growth (Guided FP-growth) algorithm, a novel method for finding the count of a given list of itemsets in large data. Unlike FPgrowth, our algorithm is designed to focus on the specific multiple itemsets of interest and hence its time and memory costs are better. 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 multi-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 superior performance using simulations and real data.