# Association rule mining and itemset-correlation based variants

## TL;DR

This paper discusses association rule mining, focusing on the apriori algorithm and its variants that handle quantitative attributes and generalizations while maintaining efficient pruning capabilities.

## Contribution

It introduces variants of the apriori algorithm for quantitative attributes and item generalizations, preserving the pruning property for efficient rule mining.

## Key findings

- Presented the apriori algorithm as a basis for association rule mining.
- Proposed variants for handling quantitative attributes and item generalizations.
- Maintained the downward closure property in the variants for efficient pruning.

## Abstract

Association rules express implication formed relations among attributes in databases of itemsets. The apriori algorithm is presented, the basis for most association rule mining algorithms. It works by pruning away rules that need not be evaluated based on the user specified minimum support confidence. Additionally, variations of the algorithm are presented that enable it to handle quantitative attributes and to extract rules about generalizations of items, but preserve the downward closure property that enables pruning. Intertransformation of the extensions is proposed for special cases.