The dynamic fusion representation of multi-source fuzzy data

Data fusion technology plays a pivotal role in aggregating, storing, and mining multi-source data to extract its joint value through the construction of a unified fusion representation model. However, we argue that mainstream methods are limited to precise data, which may not satisfy practical application requirements, as data collected from an information source often exhibits imprecision and uncertainty. In this paper, we develop a Multi-source Fuzzy Data-driven Dynamic Fusion Representation (MFD-DFR) model. This model effectively addresses the challenges related to heterogeneity, dynamics, and quality optimization that arise during the fusion of fuzzy data. To achieve this goal, we first adopt intervals as a means of description to capture the inherent uncertainty in single-source information. We then present a Dynamic Interval Standardization (DIS) algorithm, a novel approach that dynamically deals with the heterogeneity of multi-source fuzzy data without relying on the storage of historical sample data. We next propose a fusion representation model for standardized intervals that improves the quality of single-source fuzzy data through confidence interval estimation. The experimental results convincingly demonstrate that the MFD-DFR model outperforms alternative models in terms of data classification and clustering. We also show the effectiveness of our proposed DIS algorithm in expediting the convergence speed of gradient descent algorithms.