Machine Learning and Data Mining

- Research Activities

In 2004, the cInQ project (consortium on knowledge discovery by Inductive Queries, a european funded project under the FET arm of the Fifth framework) came to its natural end with the delivery of the final version of the MINERULE prototype.
The cInQ project aimed to develop the theory (database primitives), query languages and a prototype of inductive databases, i.e., databases that collect both raw data to be analyzed and patterns extracted from data by means of data mining and machine learning techniques.
In particular, the MINERULE prototype address the problem of incremental execution of a data mining query, in order to speed-up query execution times. An incremental execution exploits the results of previous queries instead of executing from scratch.
A new research interest started around the analysis of event sequences that present interesting problems for machine learning techniques. Discovering complex events made of simple ones in sequences of events is a quite hard task that is useful in several application domains, such as log analysis, network traffic analysis, and bioinformatics.
In particular, several collaborations with biologists and physicians on bioinformatics projects have been pursued in these years. One of them resulted in a tool for retrieval of annotated non-coding regions from sequenced eukaryotic genomes, while a collaboration with S.A.F.AN. Bioinformatics, sponsored by Provincia di Torino, was aimed to develop a bioinformatics tool for modelling 3D structures of proteins.

- Publications

[1] Botta Marco, Calogero Raffaele, Cordero F, Franceschinis Giuliana, Lazzarato Fulvio. RRE, a tool for retrieval of annotated non- coding regions from sequenced eukaryotic genomes. Bioinformatics, 20(16):2848--2850. OXFORD UNIV PRESS. ISSN 1367-4803, 2004.

[2] Botta Marco, Galassi U, Giordana Attilio. Learning Complex and Sparse Events in Long Sequences. 16th Eureopean Conference on Artificial Intelligence, ECAI'2004. In Ramon López de Mántaras, Lorenza Saitta (Eds.) ed(s), pp. 425--429. IOS Press, ISBN 1-58603-452-9, 2004.

[3] Esposito Roberto. Empirical evaluation of the effects of conceptcomplexity on generalization error. 16th Eureopean Conference on Artificial Intelligence, ECAI'2004. In Ramon López de Mántaras, Lorenza Saitta ed(s), pp. 1009--1010. IOS Press, ISBN 1-58603-452-9, 2004.

[4] Esposito Roberto, Saitta Lorenza. A monte carlo analysis of ensemble classification. Twenty-first International Conference on Machine Learning (ICML 2004). In Carla E. Brodley ed(s), pp. 34--. ACM Press, ISBN 1-58113-828-5, 2004.

[5] Esposito Roberto, Saitta Lorenza. A monte carlo analysis of ensemble classification. Twenty-first International Conference on Machine Learning (ICML 2004). In Carla E. Brodley ed(s), pp. 34--. ACM Press, ISBN 1-58113-828-5, 2004.

[6] Ardissono Liliana, Bellifemine F, Botta Marco, Di Costa L, Difino A, Negro B, Petrone Giovanna. Customer Information Sharing between E-commerce Applications. WOA 2004 - Sistemi Complessi e Agenti Razionali, 2004.

[7] Klemettinen M, Lanzi P.L., Meo Rosa. Database Support for Data Mining Applications. Curatela. Lecture Notes in Computer Science, volume 2682. Springer, 2004.

[8] Botta Marco, Boulicaut J-F., Masson C, Meo Rosa. Query Languages Supporting Descriptive Rule Mining: A Comparative Study. Database Support for Data Mining Applications, Lectures Notes in Artificial Intelligence (LNAI), volume 2682, pp. 24--51. Springer, 2004.

[9] Meo Rosa, Psaila G. MINE RULE: Semantic Dimensions in Association Rule Mining. "Encyclopedia of Data Warehousing and Mining", Information Science Publishing, Idea Group, ISBN 1-59140-557-2, 2004.

[10] Botta Marco, Esposito Roberto, Meo Rosa. Query Rewriting in Itemset Mining. Flexible Query Answering Systems, Lectures Notes in Artificial Intelligence (LNAI), volume 3055, pp. 111--124. Springer, 2004.

[11] Esposito Roberto, Lanzi P.L., Matera M, Meo Rosa. Integrating Web Conceptual Modelling and Web Usage Mining. Articolo in atti informali di conferenza. CM SIGKDD Workshop on Web Mining and Web Usage Analysis, 2004.

[12] Botta Marco, Esposito Roberto, Gallo Arianna, Meo Rosa. A Novel Incremental Approach to Association Rule Mining in Inductive Databases. LNCS, volume 3848, pp. 267--294. Springer, ISBN 3-540-31331-1, 2005.

[13] Meo Rosa, Psaila G. MINE RULE: Semantic Dimensions in Association Rule Mining. "Enciclopedia of Data Warehousing and Mining". In John Wang ed(s)., Idea Group Reference, ISBN 1-59140-557-2, 2005.

[14] Botta Marco, Esposito Roberto, Gallo Arianna, Meo Rosa. Optimization of Association Rules Extraction Through Exploitation of Context Dependent Constraints. AI*IA 2005: Advances in Artificial Intelligence, 9th Congress of the Italian Association for Artificial Intelligence, Lectures Notes in Artificial Intelligence (LNAI). In Stefania Bandini, Sara Manzoni ed(s), volume 3673, pp. 258--269. Springer, ISBN 3-540-29041-9, 2005.

[15] Botta Marco, Galassi U, Giordana Attilio, Saitta Lorenza. Learning Complex Event Descriptions by Abstraction. Articolo in atti di conferenza. Nineteenth International Joint Conference on Artificial Intelligence (IJCAI'05). In Leslie Pack Kaelbling, Alessandro Saffiotti ed(s), pp. 1600--1601. Professional Book Center, ISBN 0938075934, 2005.

[16] Botta Marco, Galassi U, Giordana Attilio, Saitta Lorenza. Learning Profiles Based on Hierarchical Hidden Markov Model. 15th International Symposium on the Foundations of Intelligent Systems (ISMIS'05), Lectures Notes in Artificial Intelligence (LNAI). In Mohand-Said Hacid, Neil V. Murray, Zbigniew W. Ras, Shusaku Tsumoto ed(s), volume 3488, pp. 47--55. Springer, ISBN 3-540-25878-7, 2005.

[17] Careggio Danilo, Esposito Roberto, Lanzi P.L., Matera M, Meo Rosa. Employing Inductive Databases in Concrete Applications. European Workshop on Inductive Databases and Constraint Based Mining, Lectures Notes in Artificial Intelligence (LNAI). In Jean-François Boulicaut, Luc De Raedt, Heikki Mannila ed(s), volume 3848, pp. 295--327. Springer, ISBN 3-540-31331-1, 2005.

[18] Meo Rosa. Inductive Databases: Towards a New Generation of Databases for Knowledge Discovery. Articolo in atti di conferenza. 1st International Workshop on Integrating Data Mining, Database and Information Retrieval (IDDI), at 16th International Conference on Database and Expert Systems Applications (DEXA), pp. 1003--1007. IEEE Computer Society Press, ISBN 0-7695-2424-9, 2005.

[19] Esposito Roberto, Saitta Lorenza. Experimental comparison between bagging and Monte Carlo ensemble classification. Articolo in atti di conferenza. 22nd International Conference on Machine Learning (ICML 2005). In Luc De Raedt, Stefan Wrobel ed(s), pp. 209--216. ACM, ISBN 1-59593-180-5, 2005.

[20] Cancelliere Rosella, Slavova Angela. Dynamics and Stability of Generalized Cellular Nonlinear Network Model. Applied Mathematics and Computation, 165(1):127--136. Elsevier. ISSN 0096-3003, 2005.

[21] Cancelliere Rosella, Gai Mario. Neural network correction of astrometric chromaticity. Monthly Notices of the Royal Astronomical Society, 362(4):1483--1488. Blackwell. ISSN 0035-8711, 2005.

[22] Cancelliere Rosella. The Sensitivity Matrix of a Neural Network Performing Image Processing. 17th IMACS World Congress on Scientific Computation, Applied Mathematics and Simulation, ISBN 2-915913-02-1, 2005.

- Software Products

Research Report Year 2004 - 2005

Area 1: Computer Science

Artificial Intelligence and Human-Computer Interaction


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DIPARTIMENTO DI INFORMATICA Università di Torino

Research Report Year 2004 - 2005 Area 1: Computer Science

Artificial Intelligence and Human-Computer Interaction

Machine Learning and Data Mining

Research Report Year 2004 - 2005

Area 1: Computer Science