A scalable association rule learning and recommendation algorithm for large-scale microarray datasets

Abstract

Abstract Association rule learning algorithms have been applied to microarray datasets to find association rules among genes. With the development of microarray technology, larger datasets have been generated recently that challenge the current association rule learning algorithms. Specifically, the large number of items per transaction significantly increases the running time and memory consumption of such tasks. In this paper, we propose the Scalable Association Rule Learning (SARL) heuristic that efficiently learns gene-disease association rules and gene–gene association rules from large-scale microarray datasets. The rules are ranked based on their importance. Our experiments show the SARL algorithm outperforms the Apriori algorithm by one to three orders of magnitude.

Keywords

• Association rule learning
• Microarray dataset
• Frequent itemset mining
• Scalability
• Graph partitioning
• Apriori algorithm