Is it true that only documents in the data source are returned when running clustering or categorization?

When running clustering or categorization in a data source, it is indeed true that only the documents contained within that specific data source are returned. This process involves analyzing the contents of the data source to identify patterns, group similar documents, or categorize them based on pre-defined criteria.

This ensures that the output is a reflection of the actual data present, which is essential for maintaining accuracy and relevance in the analysis. When clustering or categorization is performed, the algorithms focus solely on the input data, meaning that extraneous or external documents that are not part of the data source will not be included in the results.

This practice supports more robust and meaningful data management, as it enables organizations to get insights rooted in their existing documents without the noise from unrelated sources. It holds significant importance in project management and data analysis tasks, where clarity and precision are critical for decision-making and planning.