A computer-implemented method for data classification in the form of automatic image classification in portals and platforms used for management of images, the method comprising: - providing a token set, which contains tokens which were generated from a plurality of field values of a plurality of data records (DR1-DR8) by tokenization, tokenization of data records for generating the tokens meaning that the data records are split into a plurality of components in accordance with a set model, the components representing the tokens, the tokens having been generated from field values of at least two different field types (F1-F7), the tokens being stored in the form of a bit sequence; - analyzing one or more features of the tokens at the level of the bit sequence in order to identify subsets (420, 422, 424) of tokens that have similar features with regard to the classification criterion of the bit-sequence length; - storing a copy of each of the subsets of feature-similar tokens identified in the analysis in a form (426, 428, 430) that is separated by subset, each subset copy representing a class of feature-similar data, - wherein the tokens are bit sequences that each encode an image, the bit-sequence length representing the image size, - wherein the subsets of the tokens contain at least one small-image subset and one large-image subset, - wherein tokens of which the bit-sequence length is below a first threshold value are selectively contained in the small-image subset, and - wherein tokens of which the bit-sequence length exceeds a second threshold value are selectively contained in the large-image subset, - such that the automatic classification of images is performed on the basis of their resolution or quality. - providing a token set, which contains tokens which were generated from a plurality of field values of a plurality of data records (DR1-DR8) by tokenization, tokenization of data records for generating the tokens meaning that the data records are split into a plurality of components in accordance with a set model, the components representing the tokens, the tokens having been generated from field values of at least two different field types (F1-F7), the tokens being stored in the form of a bit sequence; - analyzing one or more features of the tokens at the level of the bit sequence in order to identify subsets (420, 422, 424) of tokens that have similar features with regard to the classification criterion of the bit-sequence length; - storing a copy of each of the subsets of feature-similar tokens identified in the analysis in a form (426, 428, 430) that is separated by subset, each subset copy representing a class of feature-similar data, - wherein the tokens are bit sequences that each encode an image, the bit-sequence length representing the image size, - wherein the subsets of the tokens contain at least one small-image subset and one large-image subset, - wherein tokens of which the bit-sequence length is below a first threshold value are selectively contained in the small-image subset, and - wherein tokens of which the bit-sequence length exceeds a second threshold value are selectively contained in the large-image subset, - such that the automatic classification of images is performed on the basis of their resolution or quality.