Heterogeneous and voluminous data sources are common in modern datasets, particularly in systems biology studies. For instance, in multi-holistic approaches in the fruit biology field, data sources can include a mix of measurements such as morpho-agronomic traits, different kinds of molecules (nucleic acids and metabolites) and consumer preferences. These sources not only have different types of data (quantitative and qualitative), but also large amounts of variables with possibly non-linear relationships among them. An integrative analysis is usually hard to conduct, since it requires several manual standardization steps, with a direct and critical impact on the results obtained. These are important issues in clustering applications, which highlight the need of new methods for uncovering complex relationships in such diverse repositories.