Efficient association rule mining among both frequent and infrequent items

✍ Scribed by Ling Zhou; Stephen Yau

• Book ID: 104007998
• Publisher: Elsevier Science
• Year: 2007
• Tongue: English
• Weight: 842 KB
• Volume: 54
• Category: Article
• ISSN: 0898-1221
• DOI: 10.1016/j.camwa.2007.02.010

✦ Synopsis

Association rule mining among frequent items has been extensively studied in data mining research. However, in recent years, there has been an increasing demand for mining the infrequent items (such as rare but expensive items). Since exploring interesting relationship among infrequent items has not been discussed much in the literature, in this paper, we propose two simple, practical and effective schemes to mine association rules among rare items. Our algorithm can also be applied to frequent items with bounded length. Experiments are performed on the well-known IBM synthetic database. Our schemes compare favorably to Apriori and FPgrowth under the situation being evaluated.