Abstract
A growing number of organizations are adopting a strategy of breaking down large data analysis problems into specific sub-problems, tailoring models for each. However, handling a large number of individual models can pose challenges in understanding organization-wide phenomena. Recent studies focus on using decision trees to create a consensus model by aggregating local decision trees into sets of rules. Despite efforts, the resulting models may still be incomplete, i.e., not able to cover the entire decision space. This paper explores methodologies to tackle this issue by generating complete consensus models from incomplete rule sets, relying on rough estimates of the distribution of independent variables. Two approaches are introduced: synthetic dataset creation followed by decision tree training and a specialized algorithm for creating a decision tree from symbolic data. The feasibility of generating complete decision trees is demonstrated, along with an empirical evaluation on a number of datasets.

Abstract
This study explores data mining techniques for predicting student dropout in higher education. The research compares different methodological approaches, including alternative algorithms and variations in model specifications. Additionally, we examine the impact of employing either a single model for all university programs or separate models per program. The performance of models with students grouped according to their position on the program study plan was also tested. The training datasets were explored with varying time series lengths (2, 4, 6, and 8 years) and the experiments use academic data from the University of Porto, spanning the academic years from 2012 to 2022. The algorithm that yielded the best results was XGBoost. The best predictions were obtained with models trained with two years of data, both with separate models for each program and with a single model. The findings highlight the potential of data mining approaches in predicting student dropout, offering valuable insights for higher education institutions aiming to improve student retention and success. © 2023 Associacao Portuguesa de Sistemas de Informacao. All rights reserved.

Abstract
In many organizations with a distributed operation, not only is data collection distributed, but models are also developed and deployed separately. Understanding the combined knowledge of all the local models may be important and challenging, especially in the case of a large number of models. The automated development of consensus models, which aggregate multiple models into a single one, involves several challenges, including fidelity (ensuring that aggregation does not penalize the predictive performance severely) and completeness (ensuring that the consensus model covers the same space as the local models). In this paper, we address the latter, proposing two measures for geometrical and distributional completeness. The first quantifies the proportion of the decision space that is covered by a model, while the second takes into account the concentration of the data that is covered by the model. The use of these measures is illustrated in a real-world example of academic management, as well as four publicly available datasets. The results indicate that distributional completeness in the deployed models is consistently higher than geometrical completeness. Although consensus models tend to be geometrically incomplete, distributional completeness reveals that they cover the regions of the decision space with a higher concentration of data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.