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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, Manco, G., Nanni, M., Pedreschi, D., and Turini, F., “Integration of Deduction and Induction for Mining Supermarket Sales Data”, in SEBD, 1999, pp. 117-131.
F. Giannotti, Nanni, M., and Pedreschi, D., “Efficient Mining of Temporally Annotated Sequences”, in SDM, 2006.
F. Giannotti and Manco, G., “Querying Inductive Databases via Logic-Based User-Defined Aggregates”, in PKDD, 1999, pp. 125-135.
F. Giannotti, Nanni, M., Pedreschi, D., and Pinelli, F., “Mining sequences with temporal annotations”, in SAC, 2006, pp. 593-597.
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, Matteucci, A., Pedreschi, D., and Turini, F., “Symbolic Evaluation with Structural Recursive Symbolic Constants”, Sci. Comput. Program., vol. 9, pp. 161-177, 1987.
F. Giannotti, Jeansoulin, R., and Theodoridis, Y., “Beyond Current Technology: The Perspective of Three EC GIS Projects”, in DEXA Workshop, 1999, p. 510.
F. Giannotti, Lakshmanan, L. V. S., Monreale, A., Pedreschi, D., and Wang, H. Wendy, “Privacy-preserving data mining from outsourced databases.”, in the 3rd International Conference on Computers, Privacy, and Data Protection: An element of choice , 2011.
F. Giannotti and Pedreschi, D., “Declarative Semantics for Pruning Operators in Logic Programming”, in LPNMR, 1990, pp. 27-37.
F. Giannotti and Manco, G., “Querying inductive Databases via Logic-Based user-defined aggregates”, in APPIA-GULP-PRODE, 1999, pp. 605-620.
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, Rossetti, G., and Pedreschi, D., “Audio Ergo Sum”, in Federation of International Conferences on Software Technologies: Applications and Foundations, 2016.
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., Turini, F., Giannotti, F., and Pedreschi, D., “A survey of methods for explaining black box models”, ACM computing surveys (CSUR), vol. 51, p. 93, 2018.
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, Trasarti, R., and Nanni, M., “{TOSCA:} two-steps clustering algorithm for personal locations detection”, in Proceedings of the 23rd {SIGSPATIAL} International Conference on Advances in Geographic Information Systems, Bellevue, WA, USA, November 3-6, 2015, 2015.

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