Approximate Association Rule Mining

Association rule algorithms typically only identify patterns that occur in the original form throughout the database. In databases which contain many small variations in the data, potentially important discoveries may be ignored as a result. In this paper, we describe an associate rule mining algorithm that searches for approximate association rules. Our approach allows data that approximately matches the pattern to contribute toward the overall support of the pattern. This approach is also useful in processing missing data, which probabilistically contributes to the support of possibly matching patterns. Results of the AR algorithm are demonstrated using the Weka system and sample databases.