Papers in International Journals

  • Fumarola F, Pio G, Felle A E, Malerba D, Ceci M (2014). EDB: Knowledge Technologies for Ancient Greek and Latin Epigraphy. In: 9th Italian Research Conference on Digital Libraries, IRCDL 2013. COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE, vol. 385, p. 29-40, BERLIN HEIDELBERG:Springer-Verlag, ISBN: 978-364254346-3, ISSN: 1865-0929, doi: 10.1007/978-3-642-54347-0_4
  • LOGLISCI C, CECI M, MALERBA D (2013). Discovering Evolution Chains in Dynamic Networks. In: (a cura di): Appice A, Ceci M, Loglisci C, Manco G, Masciari E, Ras Z W, New Frontiers in Mining Complex Patterns First International Workshop, NFMCP 2012, Held in Conjunction with ECML/PKDD 2012, Bristol, UK, September 24, 2012, Rivesed Selected Papers. LECTURE NOTES IN COMPUTER SCIENCE, vol. 7765, p. 185-199, BERLIN HEIDELBERG:Springer-Verlag, ISBN: 978-3-642-37381-7, ISSN: 0302-9743, doi: 10.1007/978-3-642-37382-4_13
  • CECI M, LOGLISCI C, MACCHIA L, MALERBA D, QUERCIA L (2013). Document Image Understanding through Iterative Transductive Learning. In: (a cura di): AGOSTI M, ESPOSITO F, FERILLI S, FERRO N, Digital Libraries and Archives 8th Italian Research Conference, IRCDL 2012, Bari, Italy, February 9-10, 2012, Revised Selected Papers. COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE, vol. 354, p. 117-128, BERLIN HEIDELBERG:Springer-Verlag, ISBN: 978-3-642-35833-3, ISSN: 1865-0929, doi: 10.1007/978-3-642-35834-0_13
  • STOJANOVA D, DEBELJAK M, CECI M, APPICE A, MALERBA D, DŽEROSKI S (2012). Dealing with spatial autocorrelation in gene flow modeling. In: Ferenc Jordán, Sven Erik Jørgensen . Models of the Ecological Hierarchy. DEVELOPMENTS IN ENVIRONMENTAL MODELLING, vol. 25, p. 35-50, Oxford:Elsevier, ISBN: 9780444593962, ISSN: 0167-8892, doi: 10.1016/B978-0-444-59396-2.00003-1
  • APPICE A, CECI M, MALERBA D, LANZA A (2012). Learning and Transferring Geographically Weighted Regression Trees across Time. In: Martin Atzmueller, Alvin Chin, Denis Helic, Andreas Hotho. Modeling and Mining Ubiquitous Social Media. LECTURE NOTES IN COMPUTER SCIENCE, vol. 7472, p. 97-117, BERLIN:Springer, ISBN: 978-3-642-33683-6, ISSN: 0302-9743, doi: 10.1007/978-3-642-33684-3_6
  • Loglisci C, Appice A, Ceci M, Malerba D, Esposito F (2011). MBlab: Molecular Biodiversity Laboratory. In: Agosti M, Esposito F, Meghini C, Orio N. Digital Libraries and Archives 7th Italian Research Conference, IRCDL 2011. COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE, vol. 249, p. 132-135, BERLIN HEIDELBERG:Springer-Verlag, ISBN: 978-3-642-27302-5, ISSN: 1865-0929, doi: 10.1007/978-3-642-27302-5_18
  • CECI M, LOGLISCI C, SALVEMINI E, D'ELIA D, MALERBA D (2011). Mining Spatial Association Rules for Composite Motif Discovery. In: BRUNI R.. Mathematical Approaches to Polymer Sequence Analysis and Related Problems. p. 87-109, NEW YORK:Springer, ISBN: 978-1-4419-6800-5, doi: 10.1007/978-1-4419-6800-5_5
  • Ceci M, Loglisci C, Ferilli S, Malerba D (2011). Project D.A.M.A.: Document acquisition, management and archiving. In: Agosti M, Esposito F, Meghini C, Orio N. Digital Libraries and Archives 7th Italian Research Conference, IRCDL 2011. COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE, vol. 249, p. 115-118, BERLIN HEIDELBERG:Springer-Verlag, ISBN: 978-364227301-8, ISSN: 1865-0929, doi: 10.1007/978-3-642-27302-5_13
  • Ceci M, Loglisci C, Malerba D (2011). Transductive learning of logical structures from document images. In: Biba M, Xhafa F. Learning Structure and Schemas from Documents. STUDIES IN COMPUTATIONAL INTELLIGENCE, vol. 375, p. 121-142, BERLIN HEIDELBERG:Springer-Verlag, ISBN: 978-364222912-1, ISSN: 1860-949X, doi: 10.1007/978-3-642-22913-8_6
  • M. Ceci, C. Loglisci, E. Salvemini, D D’Elia & D. Malerba (2010). Mining Spatial Association Rules for Composite Motif Discovery. Chapter 5 in R. Bruni (Ed.), Mathematical Approaches to Polymer Sequence Analysis and Related Problems, , pp. 87-109, Springer.
  • M. Ceci, A. Appice & D. Malerba (2010). Transductive Learning for Spatial Data Classification. In J. Koronacki et al. (Eds.): Advances in Machine Learning I, pp. 189-207, Springer.
  • M. May, B. Berendt, A. Cornuejols, J. Gama, F. Giannotti, A. Hotho, D. Malerba, E. Menesalvas, K. Morik, R. Pedersen, L. Saitta, Y. Saygin, A. Schuster & K. Vanhoof (2009). Research Challenges in Ubiquitous Knowledge Discovery. Chapter 7 in H. Kargupta, J. Han, P.S. Yu, R. Motwani, & V. Kumar (Eds.), Next Generation Data Mining, pp. 131-150, Chapman & Hall / Crc.
  • D. Malerba, A. Lanza, & A. Appice (2009). Leveraging the power of spatial data mining to enhance the applicability of GIS technology. Chapter 10 in J. Han & R. Cohen (Eds.), Geographic Knowledge Discovery and Data Mining. 2nd Edition, pp. 258-291, CRC Press - Taylor and Francis.
  • M. Berardi, D. Malerba, R. Piredda, M. Attimonelli, G. Scioscia & P. Leo (2008). Biomedical Literature Mining for Biological Databases Annotation. Chapter 16 in E.G. Giannopoulou (Ed.), Data Mining in Medical and Biological Research, pp. 267-290, IN-TECH Publisher: Vienna.
  • D. Malerba, M. Ceci, & M. Berardi (2008). Machine Learning for Reading Order Detection in Document Image Understanding. In S. Marinai & H. Fujisawa (Eds.), Database Support for Data Mining Applications, Studies in Computational Intelligence, pp. 45-69, Springer-Verlag: Berlin.
  • D. Malerba, F. Esposito, & A. Appice (2008). Exporting symbolic objects to databases. Chapter 3 in E. Diday & M. Noirhomme-Fraiture (Eds.), Symbolic Data Analysis and the SODAS Software, pp. 123-148, John Wiley & Sons: Chichester.
  • F. Esposito, D. Malerba, & A. Appice (2008). Dissimilarity and matching. Chapter 8 in E. Diday & M. Noirhomme-Fraiture (Eds.), Symbolic Data Analysis and the SODAS Software, pp. 61-66, John Wiley & Sons: Chichester.
  • MALERBA D, CECI M (2008). Learning to Order: A Relational Approach. In: RAS Z. W., TSUMOTO S., ZIGHED D. A.. Mining Complex Data, MCD 2007. LECTURE NOTES IN COMPUTER SCIENCE, vol. 4944, p. 209-223, BERLINO:Springer Verlag, ISBN: 978-3-540-68415-2, ISSN: 0302-9743, doi: 10.1007/978-3-540-68416-9
  • MALERBA D, BERARDI M, CECI M (2007). Discovering Spatio-Textual Association Rules in Document Images. In: PONCELET P., MASSEGLIA F., MAGUELONNE T.. Data Mining Patterns: New Methods and Applications. p. 178-199, HERSHEY PA:IGI Global, ISBN: 978-1-59904-162-9, doi: 10.4018/978-1-59904-162-9.ch008
  • D. Malerba, A. Appice, & M. Ceci (2004). A Data Mining Query Language for Knowledge Discovery in a Geographical Information System. Chapter 5 in R. Meo, P. Lanzi, & M. Klemettinen (Eds.), Machine Learning in Document Analysis and Recognition, LNCS 2682, pp. 95-116, Springer-Verlag: Berlin.
  • MALERBA D., ESPOSITO F., CECI M (2002). Mining HTML pages to support document sharing in a Cooperative System. In: UNLAND R., CHAUDRI A., CHABANE D., LINDNER W.. XML-Based Data Management and Multimedia Engineering - EDBT 2002 Workshops. LECTURE NOTES IN COMPUTER SCIENCE, vol. 2490, p. 420-434, BERLIN:Springer, ISBN: 3-540-00130-1, ISSN: 0302-9743, doi: 10.1007/3-540-36128-6_25
  • D. Malerba, F. Esposito, A. Lanza, & F.A. Lisi (2001). Machine learning for information extraction from topographic maps. In H. J. Miller & J. Han (Eds.), Geographic Data Mining and Knowledge Discovery, 291-314, Taylor and Francis, London, UK.
  • F. Esposito, D. Malerba, & F.A. Lisi (2000). Matching Symbolic Objects. Chapter 8.4 in in H.-H. Bock and E. Diday (Eds.), Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data, Series: Studies in Classification, Data Analysis, and Knowledge Organization, vol. 15, Springer-Verlag:Berlin, 186-197.
  • F. Esposito, D. Malerba, & V. Tamma (2000). Dissimilarity Measures for Symbolic Objects. Chapter 8.3 in in H.-H. Bock and E. Diday (Eds.), Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data, Series: Studies in Classification, Data Analysis, and Knowledge Organization, vol. 15, Springer-Verlag:Berlin, 165-185.
  • F. Esposito, D. Malerba, V. Tamma, & H.-H. Bock (2000). Classical resemblance measures. Chapter 8.1 in in H.-H. Bock and E. Diday (Eds.), Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data, Series: Studies in Classification, Data Analysis, and Knowledge Organization, vol. 15, Springer-Verlag:Berlin, 139-152.
  • M.F. Costabile, D. Malerba, M. Hemmje, & A. Paradiso (1998). Building metaphors for supporting user interaction in multimedia databases, Chapter 3 in Y. Ioannides and W. Klas (Eds.), Visual Database Systems 4 (VDB4), 47-65, Chapman & Hall, London.
  • D. Malerba, G. Semeraro and F. Esposito (1997). A Multistrategy Approach to Learning Multiple Dependent Concepts. Chapter 4 in C.,Taylor and R., Nakhaeizadeh (Eds.), Machine Learning and Statistics: The Interface, pp. 87-106, Wiley, London, England.

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