Publications

You are here

Export 489 results:
Author Title Type [ Year(Asc)]
Filters: Discovering-temporal-regularities-retail-customers’-shopping-behavior is   [Clear All Filters]
2018
R. Guidotti and Ruggieri, S., Assessing the Stability of Interpretable Models, arXiv preprint arXiv:1810.09352, 2018.
G. Rossetti and Cazabet, R., Community Discovery in Dynamic Networks: a Survey, Journal ACM Computing Surveys, vol. 51, 2018.
L. Milli, Rossetti, G., Pedreschi, D., and Giannotti, F., Diffusive Phenomena in Dynamic Networks: a data-driven study, in International Conference on Complex Networks CompleNet, Boston March 5-8 2018, 2018.
L. Gabrielli, Fadda, D., Rossetti, G., Nanni, M., Piccinini, L., Pedreschi, D., Giannotti, F., and Lattarulo, P., Discovering Mobility Functional Areas: A Mobility Data Analysis Approach, in International Workshop on Complex Networks, 2018.
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.
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.
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.
L. Pollacci, Guidotti, R., Rossetti, G., Giannotti, F., and Pedreschi, D., The Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis, in International Conference on Smart Objects and Technologies for Social Good, 2018.
G. Amato, Candela, L., Castelli, D., Esuli, A., Falchi, F., Gennaro, C., Giannotti, F., Monreale, A., Nanni, M., Pagano, P., Pappalardo, L., Pedreschi, D., Pratesi, F., Rabitti, F., Rinzivillo, S., Rossetti, G., Ruggieri, S., Sebastiani, F., and Tesconi, M., How Data Mining and Machine Learning Evolved from Relational Data Base to Data Science, in A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years, Springer International Publishing, 2018, pp. 287–306.
L. Pollacci, Guidotti, R., Rossetti, G., Giannotti, F., and Pedreschi, D., The italian music superdiversity, Multimedia Tools and Applications, pp. 1–23, 2018.
R. Guidotti, Monreale, A., Ruggieri, S., Pedreschi, D., Turini, F., and Giannotti, F., Local Rule-Based Explanations of Black Box Decision Systems, 2018.
G. Rossetti, Milli, L., Rinzivillo, S., Sirbu, A., Pedreschi, D., and Giannotti, F., NDlib: a python library to model and analyze diffusion processes over complex networks, International Journal of Data Science and Analytics, vol. 5, pp. 61–79, 2018.
D. Pedreschi, Giannotti, F., Guidotti, R., Monreale, A., Pappalardo, L., Ruggieri, S., and Turini, F., Open the Black Box Data-Driven Explanation of Black Box Decision Systems, 2018.
R. Guidotti, Rossetti, G., Pappalardo, L., Giannotti, F., and Pedreschi, D., Personalized Market Basket Prediction with Temporal Annotated Recurring Sequences, IEEE Transactions on Knowledge and Data Engineering, 2018.
F. Pratesi, Monreale, A., Trasarti, R., Giannotti, F., Pedreschi, D., and Yanagihara, T., PRUDEnce: a system for assessing privacy risk vs utility in data sharing ecosystems, Transactions on Data Privacy, vol. 11, 2018.
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.
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.
2017
M. Atzmueller, Becker, M., Molino, A., Mueller, J., Peters, J., and Sirbu, A., Applications for Environmental Sensing in EveryAware, in Participatory Sensing, Opinions and Collective Awareness, Springer, 2017, pp. 135–155.
B. Dong, Wang, H. Wendy, Monreale, A., Pedreschi, D., Giannotti, F., and Guo, W., Authenticated Outlier Mining for Outsourced Databases, IEEE Transactions on Dependable and Secure Computing, 2017.
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. 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.
L. Pappalardo and Simini, F., Data-driven generation of spatio-temporal routines in human mobility, Data Mining and Knowledge Discovery, 2017.
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.
A. Baroni, Conte, A., Patrignani, M., and Ruggieri, S., Efficiently Clustering Very Large Attributed Graphs, arXiv preprint arXiv:1703.08590, 2017.
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$\backslash$libre open source software (FLOSS) communities, Education and Information Technologies, pp. 1–23, 2017.

Pages