Which harmonization method forms links between non-related dimensions?

The correct choice is Data Fusion, as it is designed to integrate and harmonize data from different, non-related dimensions into a cohesive dataset. This method allows for the creation of new relationships and associations between disparate data points, making them comparable and analyzable within a unified framework.

Data Fusion is particularly valuable in scenarios where multiple datasets have varying structures and contexts, enabling users to create a more comprehensive understanding of the data landscape and derive meaningful insights that would be difficult to achieve if the data remained isolated.

In contrast, Custom Classification categorizes data according to specific criteria, but it does not create links between unrelated dimensions. Patterns can identify trends or recurring themes within a singular dataset, rather than forming connections across different datasets. The Parent-Child method pertains to hierarchical relationships within related dimensions, but it is not applicable to unrelated dimensions. Thus, Data Fusion stands out as the most suitable method for linking disparate data elements effectively.