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The Fuzzy transform is ubiquitous in different research fields and applications, such as image and data compression, data mining, knowledge discovery, and the analysis of linguistic expressions. As a generalisation of the Fuzzy transform, we introduce the continuous Fuzzy transform and its inverse, as an integral operator induced by a kernel function. Through the relation between membership functions and integral kernels, we show that the main properties (e.g., continuity, symmetry) of the membership functions are inherited by the continuous Fuzzy transform. Then, the relation between the continuous Fuzzy transform and integral operators is used to introduce a data-driven Fuzzy transform, which encodes intrinsic information (e.g., structure, geometry, sampling density) about the input data. In this way, we avoid coarse fuzzy partitions, which group data into large clusters that do not adapt to their local behaviour, or a too dense fuzzy partition, which generally has cells that are not covered by the data, thus being redundant and resulting in a higher computational cost. To this end, the data-driven membership functions are defined by properly filtering the spectrum of the Laplace-Beltrami operator associated with the input data. Finally, we introduce the space of continuous Fuzzy transforms, which is useful for the comparison of different continuous Fuzzy transforms and for their efficient computation.