KDDE Chapters in International Books


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.

Data Mining Techniques in Sensor Networks

The KDDE group has recently published the book entitled Data Mining Techniques in Sensor Networks, Summarization, Interpolation and Surveillance.

Book cover

Authors: Appice, A., Ciampi, A., Fumarola, F., Malerba, D.

Introduces the trend cluster, a recently defined spatio-temporal pattern, and its use in summarizing, interpolating and identifying anomalies in sensor networks.

KDDE Template

KDDE presentations have to be based on this template.

Group members and students who are taking a degree, are invited to use it.

Powered by CMSimple| Template: ge-webdesign.de| Login