An Overview of Association Rule Mining

Abstract — Data mining is a process concerned with uncovering patterns, associations, anomalies and statistically significant structures in data. Association rule mining is a data mining task that discovers associations among items in a transactional database. Association rules have been extensively studied in the literature for their usefulness in many application domains such as recommender systems, diagnosis decisions support, telecommunication, intrusion detection, etc. Efficient discovery of such rules has been a major focus in the data mining research. This paper presents an overview of association rule mining- positive and negative association rules. Research in association rules mining has initially concentrated in solving the obvious problem of finding positive association rules; that is rules among items that exist in the stored transactions. It was only several years after that the possibility of finding also negative association rules became especially appealing and was investigated.