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F. Giannotti and Manco, G., “Making Knowledge Extraction and Reasoning Closer”, in PAKDD, 2000, pp. 360-371.
F. Giannotti and Latella, D., “Using Abstract Interpretation for Gate splitting in LOTOS Specifications”, in WSA, 1992, pp. 194-204.
F. Giannotti, Mazzoni, A., Puntoni, S., and Renso, C., “Synthetic generation of cellular network positioning data”, in GIS, 2005, pp. 12-20.
F. Giannotti and Manco, G., “Declarative Knowledge Extraction with Interactive User-Defined Aggregates”, in FQAS, 2000, pp. 435-444.
F. Giannotti, Nanni, M., and Pedreschi, D., “Logic-Based Knowledge Discovery in Databases”, in EJC, 2000, pp. 279-283.
F. Giannotti and Latella, D., “Gate Splitting in LOTOS Specifications Using Abstract Interpretation”, in TAPSOFT, 1993, pp. 437-452.
F. Giannotti, Nanni, M., and Pedreschi, D., “Efficient Mining of Temporally Annotated Sequences”, in SDM, 2006.
F. Giannotti, Pedreschi, D., and Zaniolo, C., “Semantics and Expressive Power of Nondeterministic Constructs in Deductive Databases”, J. Comput. Syst. Sci., vol. 62, pp. 15-42, 2001.
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, Nanni, M., Pedreschi, D., and Pinelli, F., “Mining sequences with temporal annotations”, in SAC, 2006, pp. 593-597.
F. Giannotti, Manco, G., Nanni, M., and Pedreschi, D., “Nondeterministic, Nonmonotonic Logic Databases”, IEEE Trans. Knowl. Data Eng., vol. 13, pp. 813-823, 2001.
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, 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, 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.

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