  In the big data era, integrating diverse data modalities poses significant
challenges, particularly in complex fields like healthcare. This paper
introduces a new process model for multimodal Data Fusion for Data Mining,
integrating embeddings and the Cross-Industry Standard Process for Data Mining
with the existing Data Fusion Information Group model. Our model aims to
decrease computational costs, complexity, and bias while improving efficiency
and reliability. We also propose "disentangled dense fusion", a novel embedding
fusion method designed to optimize mutual information and facilitate dense
inter-modality feature interaction, thereby minimizing redundant information.
  We demonstrate the model's efficacy through three use cases: predicting
diabetic retinopathy using retinal images and patient metadata, domestic
violence prediction employing satellite imagery, internet, and census data, and
identifying clinical and demographic features from radiography images and
clinical notes. The model achieved a Macro F1 score of 0.92 in diabetic
retinopathy prediction, an R-squared of 0.854 and sMAPE of 24.868 in domestic
violence prediction, and a macro AUC of 0.92 and 0.99 for disease prediction
and sex classification, respectively, in radiological analysis.
  These results underscore the Data Fusion for Data Mining model's potential to
significantly impact multimodal data processing, promoting its adoption in
diverse, resource-constrained settings.
