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Author Title [ Type(Desc)] Year
Journal Article
A. Sirbu, Crane, M., and Ruskin, H. J., Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks, Microarrays, vol. 4, pp. 255–269, 2015.
R. Pellungrini, Pappalardo, L., Pratesi, F., and Monreale, A., A Data Mining Approach to Assess Privacy Risk in Human Mobility Data, ACM Trans. Intell. Syst. Technol., vol. 9, pp. 31:1–31:27, 2017.
D. Janssens, Giannotti, F., Nanni, M., Pedreschi, D., and Rinzivillo, S., Data Science for Simulating the Era of Electric Vehicles, KI - Künstliche Intelligenz, 2012.
L. Pappalardo and Simini, F., Data-driven generation of spatio-temporal routines in human mobility, Data Mining and Knowledge Discovery, 2017.
F. Giannotti and Pedreschi, D., Datalog with Non-Deterministic Choice Computers NDB-PTIME, J. Log. Program., vol. 35, pp. 79-101, 1998.
M. Aldinucci, Ruggieri, S., and Torquati, M., Decision tree building on multi-core using FastFlow, Concurrency and Computation: Practice and Experience, vol. 26, pp. 800–820, 2014.
S. Bruestle, Pappalardo, L., and Guidotti, R., Defining Geographic Markets from Probabilistic Clusters: A Machine Learning Algorithm Applied to Supermarket Scanner Data, Available at SSRN 3452058, 2019.
C. R. Daniel Ornellana, Developing a Spatial Knowledge Representation for Pedestrian Interactions, 2009.
B. Furletti, Trasarti, R., Cintia, P., and Gabrielli, L., Discovering and Understanding City Events with Big Data: The Case of Rome, Information, vol. 8, p. 74, 2017.
R. Guidotti, Gabrielli, L., Monreale, A., Pedreschi, D., and Giannotti, F., Discovering temporal regularities in retail customers’ shopping behavior, EPJ Data Science, vol. 7, p. 6, 2018.
S. Rinzivillo, Mainardi, S., Pezzoni, F., Coscia, M., Giannotti, F., and Pedreschi, D., Discovering the Geographical Borders of Human Mobility, KI - Künstliche Intelligenz, 2012.
R. Trasarti, Olteanu-Raimond, A. - M., Nanni, M., Couronné, T., Furletti, B., Giannotti, F., Smoreda, Z., and Ziemlicki, C., Discovering urban and country dynamics from mobile phone data with spatial correlation patterns, Telecommunications Policy, p. -, 2014.
S. Hajian, Domingo-Ferrer, J., Monreale, A., Pedreschi, D., and Giannotti, F., Discrimination- and privacy-aware patterns, Data Min. Knowl. Discov., vol. 29, pp. 1733–1782, 2015.
A. Romei, Ruggieri, S., and Turini, F., Discrimination discovery in scientific project evaluation: A case study, Expert Systems with Applications, vol. 40, pp. 6064–6079, 2013.
M. Nanni, Trasarti, R., Monreale, A., Grossi, V., and Pedreschi, D., Driving Profiles Computation and Monitoring for Car Insurance CRM, Journal ACM Transactions on Intelligent Systems and Technology (TIST), vol. 8, pp. 14:1–14:26, 2016.
A. Brogi, Renso, C., and Turini, F., Dynamic Composition of Parameterised Logic Modules, Computer Languages, pp. 211–242, 1999.
A. Brogi, Renso, C., and Turini, F., Dynamic composition of parameterised logic modules, Comput. Lang., vol. 25, pp. 211-242, 1999.
A. Rossi, Pappalardo, L., Cintia, P., F Iaia, M., Fernàndez, J., and Medina, D., Effective injury forecasting in soccer with GPS training data and machine learning, PloS one, vol. 13, p. e0201264, 2018.
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.
A. Baroni, Conte, A., Patrignani, M., and Ruggieri, S., Efficiently Clustering Very Large Attributed Graphs, arXiv preprint arXiv:1703.08590, 2017.
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.
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.
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.
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.
G. Rossetti, Milli, L., Giannotti, F., and Pedreschi, D., Forecasting success via early adoptions analysis: A data-driven study, PloS one, vol. 12, p. e0189096, 2017.

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