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R. Guidotti, Monreale, A., and Rinzivillo, S., Learning Data Mining, in 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), 2018.
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 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, Monreale, A., Ruggieri, S., Naretto, F., Turini, F., Pedreschi, D., and Giannotti, F., Stable and actionable explanations of black-box models through factual and counterfactual rules, Data Mining and Knowledge Discovery, 2022.
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, 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, Monreale, A., Ruggieri, S., Pedreschi, D., Turini, F., and Giannotti, F., Local Rule-Based Explanations of Black Box Decision Systems, 2018.
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, 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 Rossetti, G., “Know Thyself” How Personal Music Tastes Shape the Last. Fm Online Social Network, in International Symposium on Formal Methods, 2019.
R. Guidotti and Ruggieri, S., Assessing the Stability of Interpretable Models, arXiv preprint arXiv:1810.09352, 2018.
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, 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, Coscia, M., Pedreschi, D., and Pennacchioli, D., Going Beyond GDP to Nowcast Well-Being Using Retail Market Data, in Advances in Network Science, Springer International Publishing, 2016, pp. 29–42.
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 and Berlingerio, M., Where Is My Next Friend? Recommending Enjoyable Profiles in Location Based Services, in Complex Networks VII, Springer International Publishing, 2016, pp. 65–78.
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.
B. Guidi, Michienzi, A., and Rossetti, G., Towards the dynamic community discovery in decentralized online social networks, Journal of Grid Computing, vol. 17, pp. 23–44, 2019.
B. Guidi, Michienzi, A., and Rossetti, G., Dynamic community analysis in decentralized online social networks, in European Conference on Parallel Processing, 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, 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.
V. Grossi, Monreale, A., Nanni, M., Pedreschi, D., and Turini, F., Clustering Formulation Using Constraint Optimization, in Software Engineering and Formal Methods - {SEFM} 2015 Collocated Workshops: ATSE, HOFM, MoKMaSD, and VERY*SCART, York, UK, September 7-8, 2015, Revised Selected Papers, 2015.
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.
P. Gravino, Sirbu, A., Becker, M., Servedio, V. D. P., and Loreto, V., Experimental Assessment of the Emergence of Awareness and Its Influence on Behavioral Changes: The Everyaware Lesson, in Participatory Sensing, Opinions and Collective Awareness, Springer, 2017, pp. 337–362.

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