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R. Guidotti and Ruggieri, S., On The Stability of Interpretable Models, in 2019 International Joint Conference on Neural Networks (IJCNN), 2019.
R. Guidotti, Monreale, A., Nanni, M., Giannotti, F., and Pedreschi, D., Clustering Individual Transactional Data for Masses of Users, in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017.
R. Guidotti, Coscia, M., Pedreschi, D., and Pennacchioli, D., Behavioral Entropy and Profitability in Retail, in IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA'2015), Paris, 2015.
R. Guidotti and Coscia, M., On the Equivalence Between Community Discovery and Clustering, in International Conference on Smart Objects and Technologies for Social Good, 2017.
R. Guidotti, Nanni, M., Rinzivillo, S., Pedreschi, D., and Giannotti, F., Never drive alone: Boosting carpooling with network analysis, Information Systems, vol. 64, pp. 237–257, 2017.
R. Guidotti, Gabrielli, L., Monreale, A., Pedreschi, D., and Giannotti, F., Discovering temporal regularities in retail customers’ shopping behavior, EPJ Data Science, vol. 7, p. 6, 2018.
R. Guidotti, Sassi, A., Berlingerio, M., Pascale, A., and Ghaddar, B., Social or green? A data-driven approach for more enjoyable carpooling, in Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on, 2015.
R. Guidotti, Rossetti, G., Pappalardo, L., Giannotti, F., and Pedreschi, D., Next Basket Prediction using Recurring Sequential Patterns, arXiv preprint arXiv:1702.07158, 2017.
R. Guidotti, Monreale, A., Ruggieri, S., Pedreschi, D., Turini, F., and Giannotti, F., Local Rule-Based Explanations of Black Box Decision Systems, 2018.
R. Guidotti and Cintia, P., Towards a Boosted Route Planner Using Individual Mobility Models, in Software Engineering and Formal Methods, Springer Berlin Heidelberg, 2015, pp. 108–123.
R. Guidotti, Rossetti, G., Pappalardo, L., Giannotti, F., and Pedreschi, D., Personalized Market Basket Prediction with Temporal Annotated Recurring Sequences, IEEE Transactions on Knowledge and Data Engineering, 2018.
R. Guidotti and Ruggieri, S., Assessing the Stability of Interpretable Models, arXiv preprint arXiv:1810.09352, 2018.
R. Guidotti, Monreale, A., Rinzivillo, S., Pedreschi, D., and Giannotti, F., Retrieving Points of Interest from Human Systematic Movements, in Software Engineering and Formal Methods, Springer International Publishing, 2014, pp. 294–308.
R. Guidotti, Soldani, J., Neri, D., and Brogi, A., Explaining successful docker images using pattern mining analysis, in Federation of International Conferences on Software Technologies: Applications and Foundations, 2018.
R. Guidotti, Mobility Ranking - Human Mobility Analysis Using Ranking Measures, 2013.
R. Guidotti, Monreale, A., Giannotti, F., Pedreschi, D., Ruggieri, S., and Turini, F., Factual and Counterfactual Explanations for Black Box Decision Making, IEEE Intelligent Systems, 2019.
R. Guidotti, Rossetti, G., Pappalardo, L., Giannotti, F., and Pedreschi, D., Market Basket Prediction using User-Centric Temporal Annotated Recurring Sequences, in 2017 IEEE International Conference on Data Mining (ICDM), 2017.
R. Guidotti, Soldani, J., Neri, D., Brogi, A., and Pedreschi, D., Helping your docker images to spread based on explainable models, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2018.
R. Guidotti, Monreale, A., Rinzivillo, S., Pedreschi, D., and Giannotti, F., Unveiling mobility complexity through complex network analysis, Social Network Analysis and Mining, vol. 6, p. 59, 2016.
R. Guidotti, Trasarti, R., Nanni, M., Giannotti, F., and Pedreschi, D., There's A Path For Everyone: A Data-Driven Personal Model Reproducing Mobility Agendas, in 4th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2017), Tokyo, 2017.
R. Guidotti and Gabrielli, L., Recognizing Residents and Tourists with Retail Data Using Shopping Profiles, in International Conference on Smart Objects and Technologies for Social Good, 2017.
V. Grossi, Romei, A., and Ruggieri, S., A Case Study in Sequential Pattern Mining for IT-Operational Risk, in ECML/PKDD (1), 2008, pp. 424-439.
V. Grossi, Pedreschi, D., and Turini, F., Data Mining and Constraints: An Overview, in Data Mining and Constraint Programming, Springer International Publishing, 2016, pp. 25–48.
V. Grossi, Romei, A., and Turini, F., Survey on using constraints in data mining, Data Mining and Knowledge Discovery, vol. 31, pp. 424–464, 2017.
V. Grossi, Guns, T., Monreale, A., Nanni, M., and Nijssen, S., Partition-Based Clustering Using Constraint Optimization, in Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach, Springer International Publishing, 2016, pp. 282–299.

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