Data warehouses usually have some missing values due to unavailable data that affect the number and the quality of the generated rules. The missing values could affect the coverage percentage and number of reduces generated from a specific data set. Missing values lead to the difficulty of extracting useful information from data set. Association rule algorithms typically only identify patterns that occur in the original form throughout the database. Handling Missing Values for Association Rule Mining 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.
