What is the recommended rule for the number of occurrences in a Repeat Content Identification set?

The recommended rule for the number of occurrences in a Repeat Content Identification set is based on balancing efficiency and effectiveness in identifying repetitive data within a dataset. Setting this threshold at 400 per 100,000 documents ensures that a sufficient number of occurrences are detected to make the set useful for analytics and reporting without overwhelming the system with excessive data points.

Specifically, this recommendation arises from the need to optimize the performance of the Repeat Content Identification process. By identifying sets that contain this optimal number of occurrences, users can effectively filter and analyze relevant content, thus improving the accuracy and relevance of their findings. This threshold allows for a comprehensive analysis while maintaining system efficiency, facilitating a more manageable scope of data for review and reporting purposes.

The options with values lower or higher than 400 might fail to capture enough relevant repetitive data or may create an excess of data points that could complicate analysis processes. Thus, establishing 400 occurrences as the benchmark strikes an ideal balance for successful data identification and subsequent analysis.