Publications

You are here

Home » Research
Export 5 results:
Author [ Title(Desc)] Type Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
E
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.
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.
A. Raffaetà, Turini, F., and Renso, C., “Enhancing GISs for spatio-temporal reasoning”, in ACM-GIS, 2002, pp. 42-48.
S. Ruggieri, “Enumerating Distinct Decision Trees”, in International Conference on Machine Learning, 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.
A. Rossi, Pedreschi, D., Clifton, D. A., and Morelli, D., “Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts”, Sensors, vol. 20, p. 7122, 2020.
V. Voukelatou, Pappalardo, L., Gabrielli, L., and Giannotti, F., “Estimating countries’ peace index through the lens of the world news as monitored by GDELT”, in 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 2020.
F. Lillo and Ruggieri, S., “Estimating the Total Volume of Queries to a Search Engine”, IEEE Transactions on Knowledge and Data Engineering, pp. 1-1, 2021.
N. Forgó, Hänold, S., van den Hoven, J., Krügel, T., Lishchuk, I., Mahieu, R., Monreale, A., Pedreschi, D., Pratesi, F., and van Putten, D., “An ethico-legal framework for social data science”, 2020.
S. Citraro and Rossetti, G., “Eva: Attribute-Aware Network Segmentation”, in International Conference on Complex Networks and Their Applications, 2019.
R. Cazabet, Boudebza, S., and Rossetti, G., “Evaluating community detection algorithms for progressively evolving graphs”, arXiv preprint arXiv:2007.08635, 2020.
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., “ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraint”, in ICDM, 2003, pp. 11-18.
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., “Exante: A Preprocessing Method for Frequent-Pattern Mining”, IEEE Intelligent Systems, vol. 20, pp. 25-31, 2005.
F. Bonchi, Giannotti, F., Mazzanti, A., and Pedreschi, D., “ExAnte: Anticipated Data Reduction in Constrained Pattern Mining”, in PKDD, 2003, pp. 59-70.
G. Rossetti, “Exorcising the Demon: Angel, Efficient Node-Centric Community Discovery”, in International Conference on Complex Networks and Their Applications, 2019.
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.
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. Bodria, Panisson, A., Perotti, A., and Piaggesi, S., “Explainability Methods for Natural Language Processing: Applications to Sentiment Analysis.”, in SEBD, 2020.
F. Bodria, “Explaining Black Box with visual exploration of Latent Space”, in EuroVis, 2022.
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.
O. Lampridis, Guidotti, R., and Ruggieri, S., “Explaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars”, in Discovery Science, Cham, 2020.

Pages