Mining Approximate Functional Dependencies from Databases Based on Minimal Cover and Equivalent Classes

Data Mining (DM) represents the process of extracting interesting and previously unknown knowledge from data. Approximate Functional Dependencies (AFD) mined from database relations represent potentially interesting patterns and have proven to be useful for various tasks like feature selection for classification, query optimization and query rewriting. The discovery of AFDs still remains under explored, posing a special set of challenges. Such challenges include defining right interestingness measures for AFDs, employing effective pruning strategies and performing an efficient traversal in the search space of the attribute lattice. In this paper, we present a new algorithm for finding approximate functional dependencies from large relational databases, based on an approximation measure g3. This algorithm utilizes some concepts from relational databases design theory specifically the concepts of equivalences and the minimal cover. It has resulted in large improvement in performance in comparison with a modified version of an algorithm called TANE.