Abstract: Computer software architectures are disclosed that use improved machine learning techniques for data science and data clustering. Computer operations are improved by more efficiently and effectively processing relevant data. Based on a clustering model, initial clusters of taxonomical pairs of entity classifications and entity sub-classifications using taxonomical-level textual data representative of one or more aspects of electronic transactions associated with the taxonomical pairs can be determined, wherein the clustering model has been generated based on machine learning applied to past clusters of past taxonomical pairs of entity classifications and entity sub-classifications other than the initial clusters of the taxonomical pairs, and iteratively refining the initial clusters, according to a similarity criterion, resulting in tuned clusters.