# A Guided FP-growth algorithm for multitude-targeted mining of big data

arXiv: https://arxiv.org/abs/1803.06632 · HTML: https://mlsanity.com/paper/1803.06632/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2018.

## Built on by (most cited first, 2 of 2)

- [TargetUM: Targeted High-Utility Itemset Querying](https://mlsanity.com/paper/2111.00309/) (2021)
- [Towards Target High-Utility Itemsets](https://mlsanity.com/paper/2206.06157/) (2022)

## Abstract (arXiv)

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.