SCR-Apriori for Mining `Sets of Contrasting Rules'
This work addresses a computational bottleneck for researchers and practitioners in data mining by providing a more efficient method for mining SCR-patterns, though it is incremental as it builds on the Apriori algorithm.
The paper tackles the problem of efficiently mining Sets of Contrasting Rules (SCR-patterns), which are high-quality association rule sets, by proposing the SCR-Apriori algorithm that reduces computational expense while producing the same patterns as state-of-the-art methods.
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.