Data Mining and Knowledge Discovery Group at the Department of Computer Science and Engineering (DISI) of the University of Bologna

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- Gabriele Monti
- Gianfranco Cellarosi

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- GenData 2020

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Publications

A list of recent publications of the Data Mining and Knowledge Discovery Group at DISI follows, grouped by topic.

Gene Ontologies

Domeniconi, G., Masseroli, M., Moro, G., & Pinoli, P. (2016). Cross-organism learning method to discover new gene functionalities. Computer Methods and Programs in Biomedicine, 126, 20-34. https://doi.org/10.1016/j.cmpb.2015.12.002
Lena, P. D., Domeniconi, G., Margara, L., & Moro, G. (2015). GOTA: GO term annotation of biomedical literature. BMC Bioinformatics, 16(1), 346. https://doi.org/10.1186/s12859-015-0777-8
Domeniconi, G., Masseroli, M., Moro, G., & Pinoli, P. (2014). Discovering New Gene Functionalities from Random Perturbations of Known Gene Ontological Annotations. In KDIR 2014 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, Rome, Italy, 21 - 24 October, 2014 (pp. 107-116). https://doi.org/10.5220/0005087801070116
Domeniconi, G., Masseroli, M., Moro, G., & Pinoli, P. (2014). Random Perturbations of Term Weighted Gene Ontology Annotations for Discovering Gene Unknown Functionalities. In Knowledge Discovery, Knowledge Engineering and Knowledge Management - 6th International Joint Conference, IC3K 2014, Rome, Italy, October 21-24, 2014, Revised Selected Papers (pp. 181-197). https://doi.org/10.1007/978-3-319-25840-9_12

Support Vector Models and optimization in distributed and streaming environments

Frandi, E., Ñanculef, R., Lodi, S., Sartori, C., & Suykens, Johan A. K. (2016). Fast and Scalable Lasso via Stochastic Frank-Wolfe Methods with a Convergence Guarantee. Machine Learning 104(2), 195-221. Berlin Heidelberg: Springer.
Frandi, E., Ñanculef, R., Gasparo, M. G., Lodi, S., & Sartori, C. (2013).Training Support Vector Machines Using Frank-Wolfe Optimization Methods. International Journal of Pattern Recognition and Artificial Intelligence 27(3), 1360003. Singapore: World Scientific.
Ñanculef, R., Allende, H., Lodi, S., & Sartori, C. Two One-Pass Algorithms for Data Stream Classification using Approximate MEBs. In A. Dobnikar, U. Lotrič, & B. Šter (Eds.), Adaptive and Natural Computing Algorithms, 10th International Conference, ICANNGA'11, Proceedings, Part II, April 14-16, 2011, volume 6594 of Lecture Notes in Computer Science (pp. 363-372). Berlin Heidelberg: Springer.
Frandi, E., Gasparo, M. G., Lodi, S., Ñanculef, R., & Sartori, C. (2010). A New Algorithm for Training SVMs using Approximate Minimal Enclosing Balls. In I. Bloch & R. M. Cesar (Eds.), Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 15th Iberoamerican Congress on Pattern Recognition, CIARP 2010, Sao Paulo, Brazil, November 8-11, 2010, volume 6419 of Lecture Notes in Computer Science (pp. 87-95). Berlin Heidelberg: Springer. The paper was awarded as the "Best Paper" of the conference.
Lodi, S., Ñanculef, R., & Sartori, C. (2010). Single-Pass Distributed Learning of Multi-class SVMs Using Core-Sets. Proceedings of the Tenth SIAM International Conference on Data Mining, Columbus, Ohio, April 29-May 1, 2010 (pp. 257-268). Philadelphia: SIAM.
Lodi, S., Ñanculef, R., & Sartori, C. (2009). L2-SVM Training with Distributed Data. In L. Braubach, W. van der Hoek, P. Petta, & A. Pokahr (Eds.), Multiagent System Technologies, 7th German Conference, MATES 2009, Hamburg, Germany, September 9-11, 2009, volume 5774 of Lecture Notes in Computer Science (pp. 208-213). Berlin Heidelberg: Springer.

Parallel and Distributed Outlier Detection

Angiulli, F., Basta, S., Lodi, S., & Sartori, C. (2016). GPU Strategies for Distance-based Outlier Detection. IEEE Transactions on Parallel and Distributed Systems 27(11), 3256-3268. Los Alamitos, CA: IEEE Computer Society.
Lodi, S., Angiulli, F., Basta, S., Luiselli, D., Pagani, L., & Sartori, C. (2014). Distance-based outlier approach to improve the detection of genomic loci undergoing differentiation in worldwide human populations. Accepted for publication in a volume of selected papers from Workshop Bringing Maths to Life (BMTL 2014), Naples, Italy, October 27-29, 2014. Berlin Heidelberg: Springer.
Angiulli, F., Basta, S., Lodi, S., & Sartori, C. (2014). Accelerating outlier detection with intra- and inter-node parallelism. HPPD-DM 2014, Special Session on High Performance Parallel and Distributed Data Mining, International Conference on High Performance Computing & Simulation (HPCS 2014), Bologna, Italy, July 21-25, 2013 (pp. 143-150).
Angiulli, F., Basta, S., Lodi, S., & Sartori, C. (2013). Fast outlier detection using a GPU. HPPD-DM 2013, Special Session on High Performance Parallel and Distributed Data Mining, International Conference on High Performance Computing & Simulation (HPCS 2013), Helsinki, Finland, July 1-5, 2013 (pp. 143-150). Los Alamitos, CA: IEEE Computer Society.
Angiulli, F., Basta, S., Lodi, S., & Sartori, C. (2013). Distributed Strategies for Mining Outliers in Large Data Sets. IEEE Transactions on Knowledge and Data Engineering 25(7), 1520-1532. Los Alamitos, CA: IEEE Computer Society.
Angiulli, F., Basta, S., Lodi, S., & Sartori, C. (2010). A Distributed Approach to Detect Outliers in Very Large Data Sets. In P. D'Ambra, M. Guarracino, D. Talia (Eds.), Euro-Par 2010 - Parallel Processing, Proceedings, Part I, 16th International Euro-Par Conference, Ischia, Italy, August 31-September 3, 2010 (pp. 329-340). Berlin Heidelberg: Springer.

Distributed Data Clustering

Costa da Silva, J., Klusch, M., & Lodi, S. (2016). Privacy-awareness of Distributed Data Clustering Algorithms Revisited. In Henrik Boström, Arno Knobbe, Carlos Soares, & Panagiotis Papapetrou (eds.), Advances in Intelligent Data Analysis XV, October 13-15, 2016, Stockholm, volume 9897 of Lecture Notes in Computer Science (pp. 261-272). Cham: Springer International Publishing.
Costa da Silva, J., Klusch, M., Lodi, S., & Moro, G. (2006). Privacy-preserving agent-based distributed data clustering. Web Intelligence and Agent Systems 4, 221-238.
Bellavia, S., Lodi, S., & Morini, B. (2006). Inferences on Kernel Density Estimates by Solving Nonlinear Systems. In K. A. Froeschl & W. Grossmann (Eds.), Proceedings 18th International Conference on Scientific and Statistical Database Management SSDBM 2006, Vienna, Austria, 3-5 July 2006 (pp. 389-397). Los Alamitos, California: IEEE Computer Society.
Costa da Silva, J., Klusch, M., Lodi, S., & Moro, G. (2004). Inference Attacks in Peer-to-Peer Homogeneous Distributed Data Mining. In R. López de Mántaras & L. Saitta (Eds.), Proceedings of the 16th Eureopean Conference on Artificial Intelligence, ECAI 2004, including Prestigious Applicants of Intelligent Systems, PAIS 2004, Valencia, Spain, August 22-27, 2004 (pp. 450-454). IOS Press.
Klusch, M., Lodi, S., & Moro, G. (2003). Issues of agent-based distributed data mining. In Proceedings of the Second International Joint Conference on Autonomous Agents & Multiagent Systems, AAMAS 2003, July 14-18, 2003, Melbourne, Victoria, Australia, (pp. 1034-1035). New York, NY: ACM.
Klusch, M., Lodi, S., & Moro, G. (2003). Agent-Based Distributed Data Mining: The KDEC Scheme. In M. Klusch, S. Bergamaschi, P. Edwards, & P. Petta (Eds.), Intelligent Information Agents, The AgentLink Perspective, volume 2586 of Lecture Notes in Computer Science (pp. 104-122). Berlin Heidelberg: Springer.
Klusch, M., Lodi, S., & Moro, G. (2003). The Role of Agents in Distributed Data Mining: Issues and Benefits. IEEE/WIC International Conference on Intelligent Agent Technology (IAT 2003), 13-17 October 2003 (pp. 211-217). Halifax, Canada: IEEE Computer Society.
Klusch, M., Lodi, S., & Moro, G. (2003). Distributed clustering based on sampling local density estimates. In Proceedings of the 19th International Joint Conference on Artificial Intelligence (pp. 485-490). Acapulco, Mexico: AAAI Press.

Stream Data Clustering

Lodi, S., Moro, G., & Sartori, C. (2006). Stream Clustering Based on Kernel Density Estimation. In G. Brewka, S. Coradeschi, A. Perini, P. Traverso (Eds.), Proceedings of the 17th Eureopean Conference on Artificial Intelligence, ECAI 2006, Including Prestigious Applications of Intelligent Systems (PAIS 2006), Riva del Garda, Italy, August 29 - September 1, 2006 (pp. 799-800). IOS Press.

Data management and mining in massive and self-administered networks

Cerroni, W., Monti, G., Moro, G., & Ramilli, M.: Network Attack Detection Based on Peer-to-Peer Clustering of SNMP Data. In N. Bartolini, S. Nikoletseas, P. Sinha, V. Cardellini, & A. Mahanti (Eds.), Quality of Service in Heterogeneous Networks, Proceedings of the 6th International ICST Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2009 and 3rd International Workshop on Advanced Architectures and Algorithms for Internet Delivery and Applications, AAA-IDEA 2009, Las Palmas, Gran Canaria, November 23-25, 2009, volume 22 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (pp. 417-430). Berlin Heidelberg: Springer.
Monti, G., & Moro, G. (2009). Self-organization and Local Learning Methods for Improving the Applicability and Efficiency of Data-Centric Sensor Networks. In N. Bartolini, S. Nikoletseas, P. Sinha, V. Cardellini, & A. Mahanti (Eds.), Quality of Service in Heterogeneous Networks, Proceedings of the 6th International ICST Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2009 and 3rd International Workshop on Advanced Architectures and Algorithms for Internet Delivery and Applications, AAA-IDEA 2009, Las Palmas, Gran Canaria, November 23-25, 2009, volume 22 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (pp. 627-643). Berlin Heidelberg: Springer.
Lodi, S., Monti, G., Moro, G., & Sartori, C. (2009). Peer-to-Peer Data Clustering in Self-Organizing Sensor Networks. In A. Cuzzocrea (Ed.), Intelligent Techniques for Warehousing and Mining Sensor Network Data (pp. 179-212). Hershey, PA: Information Science Reference.
Monti, G., & Moro, G. (2008). Multidimensional Range Query and Load Balancing in Wireless Ad Hoc and Sensor Networks. In K. Wehrle, W. Kellerer, S. K. Singhal, & R. Steinmetz (Eds.), Proceedings of the Eighth International Conference on Peer-to-Peer Computing, 8-11 September 2008, Aachen, Germany (pp. 205-214). Los Alamitos, California: IEEE Computer Society.
Monti, G., Moro, G., & Lodi, S. (2007). W*-Grid: A Robust Decentralized Cross-layer Infrastructure for Routing and Multi-Dimensional Data Management in Wireless Ad-Hoc Sensor Networks. In M. Hauswirth, A. Montresor, N. Shahmehri, K. Wehrle, & A. Wierzbicki, Proceedings of the Seventh International Conference on Peer-to-Peer Computing, 2-5 September 2007, Galway, Ireland (pp. 159-166). Los Alamitos: IEEE Computer Society.
Moro, G., Monti, G., & Sartori, C. (2006).W^R-Grid: A Scalable Cross-Layer Infrastructure for Routing, Multi-dimensional Data Management and Replication in Wireless Sensor Networks.In G. Min, B. Di Martino, L. T. Yang, M. Guo, & G. Rünger (Eds.), Frontiers of High Performance Computing and Networking - ISPA 2006 Workshops, volume 4331 of Lecture Notes in Computer Science (pp. 377-386). Berlin Heidelberg: Springer.
Moro, G., & Monti, G. (2006). W-Grid: a Cross-Layer Infrastructure for Multi-Dimensional Indexing, Querying and Routing in Wireless Ad-Hoc and Sensor Networks.In A. Montresor, A. Wierzbicki, & N. Shahmehri, Proceedings of the Sixth IEEE International Conference on Peer-to-Peer Computing (P2P'06), 6-8 September 2006, Cambridge, UK, (pp. 210-220). Los Alamitos: IEEE Computer Society.

September, 2018