SCR-Apriori for Mining `Sets of Contrasting Rules'

TL;DR

This paper introduces SCR-Apriori, an efficient algorithm for mining Set of Contrasting Rules patterns, which are valuable for knowledge discovery due to their guaranteed quality, by significantly reducing computational costs compared to existing methods.

Contribution

The paper presents SCR-Apriori, a novel, less computationally expensive algorithm for mining SCR-patterns that maintains the same results as current approaches.

Findings

SCR-Apriori reduces search space significantly.

It produces the same SCR-patterns as state-of-the-art methods.

Experimental results confirm efficiency improvements.

Abstract

In this paper, we propose an efficient algorithm for mining novel `Set of Contrasting Rules'-pattern (SCR-pattern), which consists of several association rules. This pattern is of high interest due to the guaranteed quality of the rules forming it and its ability to discover useful knowledge. However, SCR-pattern has no efficient mining algorithm. We propose SCR-Apriori algorithm, which results in the same set of SCR-patterns as the state-of-the-art approache, but is less computationally expensive. We also show experimentally that by incorporating the knowledge about the pattern structure into Apriori algorithm, SCR-Apriori can significantly prune the search space of frequent itemsets to be analysed.