Unified descriptive language for association rules in data mining

Association rule mining in large databases is one of the most interesting data mining techniques in database communities. In a very large database, the number of discovered rules rise dramatically depending on the selection of support and confidence, and the presentation of the rules in a nice and noticeable way becomes highly challenging. Researchers have developed several tools to visualize association rules in years. However, a large number of tools cannot handle more than dozens of association rules. Furthermore, none of them can effectively manage association rules with multiple antecedents. Till now a uniform descriptive presentation technique has not been set up. We studied different descriptive techniques in the context of visualization and introduced a graph-based technique as Unified Descriptive Language for Association Rules (UDLAR). This unified descriptive language for association rule mining can be used to extract the discovered rules very efficiently.