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F. Giannotti, Nanni, M., Pedreschi, D., Pinelli, F., Renso, C., Rinzivillo, S., and Trasarti, R., “Mobility data mining: discovering movement patterns from trajectory data”, in Computational Transportation Science, 2010, pp. 7-10.
F. Giannotti, Lakshmanan, L. V. S., Monreale, A., Pedreschi, D., and Wang, H. Wendy, “Privacy-Preserving Mining of Association Rules From Outsourced Transaction Databases”, IEEE Systems Journal, 2013.
F. Giannotti, Manco, G., Pedreschi, D., and Turini, F., “Experiences with a Logic-based knowledge discovery Support Environment”, in 1999 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, 1999.
F. Giannotti and Hermenegildo, M. V., “A Technique for Recursive Invariance Detection and Selective Program Specification”, in PLILP, 1991, pp. 323-334.
F. Giannotti and Manco, G., “Making Knowledge Extraction and Reasoning Closer”, in PAKDD, 2000, pp. 360-371.
F. Giannotti, Pedreschi, D., Saccà, D., and Zaniolo, C., “Non-Determinism in Deductive Databases”, in DOOD, 1991, pp. 129-146.
F. Giannotti and Manco, G., “Declarative Knowledge Extraction with Interactive User-Defined Aggregates”, in FQAS, 2000, pp. 435-444.
F. Giannotti, Nanni, M., Pinelli, F., and Pedreschi, D., “Trajectory pattern mining”, in KDD, 2007, pp. 330-339.
F. Giannotti and Latella, D., “Using Abstract Interpretation for Gate splitting in LOTOS Specifications”, in WSA, 1992, pp. 194-204.
F. Giannotti, Pedreschi, D., and Theodoridis, Y., “Geographic privacy-aware knowledge discovery and delivery”, in EDBT, 2009, pp. 1157-1158.
F. Giannotti, Mazzoni, A., Puntoni, S., and Renso, C., “Synthetic generation of cellular network positioning data”, in GIS, 2005, pp. 12-20.
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, 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, 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.
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, “Mobility Ranking - Human Mobility Analysis Using Ranking Measures”, 2013.
R. Guidotti and Rossetti, G., ““Know Thyself” How Personal Music Tastes Shape the Last.Fm Online Social Network”, in Formal Methods. FM 2019 International Workshops, Cham, 2020.
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.
R. Guidotti, Monreale, A., and Pedreschi, D., “The AI black box Explanation Problem”, ERCIM NEWS, pp. 12–13, 2019.
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.

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