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F. Giannotti, Nanni, M., Pinelli, F., and Pedreschi, D., Trajectory pattern mining, in KDD, 2007, pp. 330-339.
F. Giannotti, Trasarti, R., Bontcheva, K., and Grossi, V., SoBigData: Social Mining & Big Data Ecosystem, in Companion of the The Web Conference 2018 on The Web Conference 2018, 2018.
F. Giannotti, Manco, G., and Pedreschi, D., A Deductive Data Model for Representing and Querying Semistructured Data, in APPIA-GULP-PRODE, 1997, pp. 129-140.
F. Giannotti, Manco, G., and Wijsen, J., Logical Languages for Data Mining, in Logics for Emerging Applications of Databases, 2003, pp. 325-361.
F. Giannotti and Pedreschi, D., Datalog with Non-Deterministic Choice Computers NDB-PTIME, J. Log. Program., vol. 35, pp. 79-101, 1998.
F. Giannotti, Manco, G., and Turini, F., Specifying Mining Algorithms with Iterative User-Defined Aggregates, IEEE Trans. Knowl. Data Eng., vol. 16, pp. 1232-1246, 2004.
F. Giannotti and Pedreschi, D., Mobility, Data Mining and Privacy - Geographic Knowledge Discovery. Springer, 2008.
F. Giannotti, Manco, G., Nanni, M., and Pedreschi, D., Query Answering in Nondeterministic, Nonmonotonic Logic Databases, in FQAS, 1998, pp. 175-187.
F. Giannotti, Mazzoni, A., Puntoni, S., and Renso, C., Synthetic generation of cellular network positioning data, in GIS, 2005, pp. 12-20.
F. Giannotti, Manco, G., Nanni, M., and Pedreschi, D., On the Effective Semantics of Nondeterministic, Nonmonotonic, Temporal Logic Databases, in CSL, 1998, pp. 58-72.
F. Giannotti, Pedreschi, D., and Turini, F., Mobility, Data Mining and Privacy the Experience of the GeoPKDD Project, in PinKDD, 2008, pp. 25-32.
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.
P. Gravino, Caminiti, S., Sirbu, A., Tria, F., Servedio, V. D. P., and Loreto, V., Unveiling Political Opinion Structures with a Web-experiment, in Proceedings of the 1st International Conference on Complex Information Systems, 2016.
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, 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.
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.
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, 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, 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 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.

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