Which term refers to the removal of inaccurate or misleading data from data sets?

The term that refers to the removal of inaccurate or misleading data from data sets is indeed data cleaning. This process is essential in ensuring that the data being used for analysis, modeling, or decision-making is of high quality and reliable. Data cleaning involves identifying errors or inconsistencies in the data, such as duplicates, inaccuracies, or outliers, and then rectifying these issues to ensure that the data reflects true and meaningful information.

In the context of data management, data cleaning is a crucial step before any further analysis or processing can take place. It aims to provide a clean and corrected version of the data, which leads to more accurate and trustworthy outcomes in any cognitive project management involving AI.

Other terms, while related to data management, do not specifically pertain to the removal of inaccurate information. For instance, data validation focuses on checking if the data meets certain criteria or standards rather than cleaning it. Data auditing involves examining the data for compliance and quality checks but does not necessarily mean correcting the data itself. Data transformation refers to the process of converting data from one format or structure to another, which again does not inherently involve correcting inaccuracies.