Patterns or associations that do not conform to the binding rules are filtered out or not considered in the final

results. This helps in extracting only the information that aligns with the desired criteria

During data mining, patterns, associations, or relationships are identified in the dataset. Binding rules are then

applied to these patterns to verify whether they meet the specified criteria

Binding rules are commonly used in various data mining tasks. For example, in market basket analysis, they can define

rules for the presence or absence of specific items in a transaction. In fraud detection, binding rules can be used to flag transactions

that deviate from expected patterns

The application of binding rules often results in more interpretable and actionable findings. It helps focus on patterns

or relationships that are of particular interest or concern

Binding rules are essentially constraints that you specify to guide the data mining process. These

constraints can be based on logical conditions, business requirements, or domain-specific knowledge

Binding rules are highly customizable to meet specific business or analytical needs. They allow data analysts to

fine-tune the mining process to obtain meaningful and relevant insights