Faculty of Information Technology Applied Science University Amman, JORDAN

---

Abstract—Traditionally, research in the area of frequent itemset mining has focused on mining market basket data. Several algorithms and techniques have been introduced in the literature for mining data represented in basket data format. The primary objective of these algorithms has been to improve the performance of the mining process. Unlike basket data representation, no algorithms exist for mining frequent itemsets and association rules in relational databases that are represented using the formal relational data model. Typical relational data can not be easily converted to basket data representation for the purpose of applying frequent itemset mining algorithms. Therefore, a need arises for algorithms that can directly be applied to data represented using the formal relational data model and for a conceptual framework for mining such data. This paper solves this problem by introducing an algorithm named RDB-MINER for mining frequent itemsets in relational databases.

Index Terms—data mining, association rule, itemset, SQL, relational database.

[PDF]

Cite: Abdallah Alashqur, "RDB-MINER: A SQL-Based Algorithm for Mining True Relational Databases," Journal of Software vol. 5, no. 9, pp. 998-1005, 2010.