A non-transitory computer-readable medium with machine-readable instructions stored thereon that, when executed by a processor: obtain a group of data records (n 0 to n 9) of a data-set; assess similarity between data records (n 0 to n 9) in the group, based on a number N of plural attributes of said data records; identify clusters (Cl 1, Cl 2) of similar data records in the group based on the assessed similarity; determine, in a multidimensional space having a number D of dimensions less than the number N, respective regions (H 1 to H 4) corresponding to different clusters (Cl 1, Cl 2) determined by the identifying, wherein a selected dimensionality-reduction method transforms data records (n 0 to n 9) into said multidimensional space; and set up a classifier to identify correspondences between entities and updated data records compared to said group, based on the regions (H 1 to H 4) in said multidimensional space that contain said updated data records after transformation thereof according to the selected dimensionality-reduction method; wherein, in the similarity evaluation, similar data records (n 0 to n 9) are identified using an unsupervised similarity-evaluation method and, in the cluster identification, clusters are identified using an unsupervised cluster-identification method; further comprising instructions to: evaluate, for each region (H 1 to H 4), the quantity of data records (n 0 to n 9) whose transformed data is determined to be within the region but proximate a boundary with an adjacent region; and in a case in which the evaluation indicates that the transformed data of a specified quantity of data records (n 0 to n 9) are proximate the boundary between a pair of adjacent regions (H 1 to H 4), perform a virtual merge of said pair of regions so that data records whose transformed data is determined to be in either of the pair of regions is classified as corresponding to the same entity but the boundaries of the adjacent regions are unchanged and separate counts are maintained of the numbers of data records whose transformed data is in each of the adjacent regions. obtain a group of data records (n 0 to n 9) of a data-set; assess similarity between data records (n 0 to n 9) in the group, based on a number N of plural attributes of said data records; identify clusters (Cl 1, Cl 2) of similar data records in the group based on the assessed similarity; determine, in a multidimensional space having a number D of dimensions less than the number N, respective regions (H 1 to H 4) corresponding to different clusters (Cl 1, Cl 2) determined by the identifying, wherein a selected dimensionality-reduction method transforms data records (n 0 to n 9) into said multidimensional space; and set up a classifier to identify correspondences between entities and updated data records compared to said group, based on the regions (H 1 to H 4) in said multidimensional space that contain said updated data records after transformation thereof according to the selected dimensionality-reduction method; wherein, in the similarity evaluation, similar data records (n 0 to n 9) are identified using an unsupervised similarity-evaluation method and, in the cluster identification, clusters are identified using an unsupervised cluster-identification method; further comprising instructions to: evaluate, for each region (H 1 to H 4), the quantity of data records (n 0 to n 9) whose transformed data is determined to be within the region but proximate a boundary with an adjacent region; and in a case in which the evaluation indicates that the transformed data of a specified quantity of data records (n 0 to n 9) are proximate the boundary between a pair of adjacent regions (H 1 to H 4), perform a virtual merge of said pair of regions so that data records whose transformed data is determined to be in either of the pair of regions is classified as corresponding to the same entity but the boundaries of the adjacent regions are unchanged and separate counts are maintained of the numbers of data records whose transformed data is in each of the adjacent regions. evaluate, for each region (H 1 to H 4), the quantity of data records (n 0 to n 9) whose transformed data is determined to be within the region but proximate a boundary with an adjacent region; and in a case in which the evaluation indicates that the transformed data of a specified quantity of data records (n 0 to n 9) are proximate the boundary between a pair of adjacent regions (H 1 to H 4), perform a virtual merge of said pair of regions so that data records whose transformed data is determined to be in either of the pair of regions is classified as corresponding to the same entity but the boundaries of the adjacent regions are unchanged and separate counts are maintained of the numbers of data records whose transformed data is in each of the adjacent regions.