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P. Mukala, Cerone, A., and Turini, F., An exploration of learning processes as process maps in FLOSS repositories. 2015.
D. Pennacchioli, Coscia, M., Rinzivillo, S., Pedreschi, D., and Giannotti, F., Explaining the PRoduct Range Effect in Purchase Data, IEEE Big Data. 2013.
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.
C. Panigutti, Guidotti, R., Monreale, A., and Pedreschi, D., Explaining multi-label black-box classifiers for health applications, in International Workshop on Health Intelligence, 2019.
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.
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, Manco, G., Pedreschi, D., and Turini, F., Experiences with a Logic-Based Knowledge Discovery Support Environment, in AI*IA, 1999, pp. 202-213.
F. Bonchi, Giannotti, F., Mazzanti, A., and Pedreschi, D., ExAnte: Anticipated Data Reduction in Constrained Pattern Mining, in PKDD, 2003, pp. 59-70.
F. Bonchi, Giannotti, F., Mazzanti, A., and Pedreschi, D., Exante: A Preprocessing Method for Frequent-Pattern Mining, IEEE Intelligent Systems, vol. 20, pp. 25-31, 2005.
F. Turini, Baglioni, M., Furletti, B., and Rinzivillo, S., Examples of Integration of Induction and Deduction in Knowledge Discovery, in Reasoning, Action and Interaction in AI Theories and Systems, 2006, pp. 307-326.
F. Turini, Baglioni, M., Furletti, B., and Rinzivillo, S., Examples of Integration of Induction and Deduction in Knowledge Discovery, in Reasoning, Action and Interaction in AI Theories and Systems, vol. 4155, 2006, pp. 307-326.
F. Bonchi, Giannotti, F., Mazzanti, A., and Pedreschi, D., ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraint, in ICDM, 2003, pp. 11-18.
M. Berlingerio, Coscia, M., Giannotti, F., Monreale, A., and Pedreschi, D., Evolving networks: Eras and turning points, Intell. Data Anal., vol. 17, pp. 27–48, 2013.
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.
S. Ruggieri, Enumerating Distinct Decision Trees, in International Conference on Machine Learning, 2017.
A. Raffaetà, Turini, F., and Renso, C., Enhancing GISs for spatio-temporal reasoning, in ACM-GIS, 2002, pp. 42-48.
P. Cintia, Pappalardo, L., and Pedreschi, D., "Engine Matters": {A} First Large Scale Data Driven Study on Cyclists' Performance, in 13th {IEEE} International Conference on Data Mining Workshops, {ICDM} Workshops, TX, USA, December 7-10, 2013, 2013.
P. Mukala, Cerone, A., and Turini, F., An empirical verification of a-priori learning models on mailing archives in the context of online learning activities of participants in free\libre open source software (FLOSS) communities, Education and Information Technologies, vol. 22, pp. 3207–3229, 2017.
A. Sirbu, Crane, M., and Ruskin, H. J., EGIA–Evolutionary Optimisation of Gene Regulatory Networks, an Integrative Approach, in Complex Networks V, Springer International Publishing, 2014, pp. 217–229.
P. Contucci, Panizzi, E., Ricci-Tersenghi, F., and Sirbu, A., Egalitarianism in the rank aggregation problem: a new dimension for democracy, Quality & Quantity, pp. 1–16, 2015.
A. Baroni, Conte, A., Patrignani, M., and Ruggieri, S., Efficiently Clustering Very Large Attributed Graphs, arXiv preprint arXiv:1703.08590, 2017.
F. Giannotti, Nanni, M., and Pedreschi, D., Efficient Mining of Temporally Annotated Sequences, in SDM, 2006.
K. S. Boeg, Ira Assent,, and Magnani, M., Efficient GPU-based skyline computation, in DAMON@SIGMOD 2013, 2013.
F. Bonchi, Giannotti, F., Mazzanti, A., and Pedreschi, D., Efficient breadth-first mining of frequent pattern with monotone constraints, Knowl. Inf. Syst., vol. 8, pp. 131-153, 2005.
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.

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