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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, Manco, G., Nanni, M., and Pedreschi, D., Nondeterministic, Nonmonotonic Logic Databases, IEEE Trans. Knowl. Data Eng., vol. 13, pp. 813-823, 2001.
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, Gabrielli, L., Pedreschi, D., and Rinzivillo, S., Understanding human mobility with big data, in Solving Large Scale Learning Tasks. Challenges and Algorithms, Springer International Publishing, 2016, pp. 208–220.
F. Giannotti, Raffaetà, A., Renso, C., and Turini, F., Complex Reasoning on Geographical Data, in SEBD, 2001, pp. 331-338.
F. Giannotti and Latella, D., Gate Splitting in LOTOS Specifications Using Abstract Interpretation, Sci. Comput. Program., vol. 23, pp. 127-149, 1994.
F. Giannotti, Gozzi, C., and Manco, G., Clustering Transactional Data, in SEBD, 2001, pp. 163-176.
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, Manco, G., and Turini, F., Specifying Mining Algorithms with Iterative User-Defined Aggregates: A Case Study, in PKDD, 2001, pp. 128-139.
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.
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.
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, 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, 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, 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.
R. Guidotti, Monreale, A., Matwin, S., and Pedreschi, D., Black Box Explanation by Learning Image Exemplars in the Latent Feature Space, in Machine Learning and Knowledge Discovery in Databases, Cham, 2020.
R. Guidotti, Monreale, A., Rinzivillo, S., Pedreschi, D., and Giannotti, F., Unveiling mobility complexity through complex network analysis, Social Network Analysis and Mining, vol. 6, p. 59, 2016.

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