Declarative Data Mining Using SQL3

Researchers convincingly argue that the ability to declaratively mine and analyze relational databases using SQL for decision support is a critical requirement for the success of the acclaimed data mining technology. Although there have been several encouraging attempts at developing methods for data mining using SQL, simplicity and efficiency still remain significant impediments for further development. In this article, we propose a significantly new approach and show that any object relational database can be mined for association rules without any restructuring or preprocessing using only basic SQL3 constructs and functions, and hence no additional machineries are necessary. In particular, we show that the cost of computing association rules for a given database does not depend on support and confidence thresholds. More precisely, the set of large items can be computed using one simple join query and an aggregation once the set of all possible meets (least fixpoint) of item set patterns in the input table is known. We believe that this is an encouraging discovery especially compared to the well known SQL based methods in the literature. Finally, we capture the functionality of our proposed mining method in a mine by SQL3 operator for general use in any relational database.