Proceedings of National Conferences

  • Stojanova D, Ceci M, Malerba D, Džeroski S (2012). Using PPI Networks in Hierarchical Multi-label Classification Trees for Gene Function Prediction. In: (a cura di): Borgwardt K, Rätsch G, Machine Learning in Systems Biology. p. 10, Basel, Switzerland, 08 - 09 September 2013
  • Pio G, Ceci M, D'Elia D, Loglisci C, Malerba D (2012). A novel biclustering algorithm for the discovery of meaningful biological correlations between miRNAs and mRNAs. EMBNET JOURNAL, vol. 18, p. 43-44, ISSN: 2226-6089
  • Pio G, Ceci M, Loglisci C, Malerba D, D'Elia D (2012). The integration of microRNA target data by biclustering techniques opens new roads for signaling networks analysis. EMBNET JOURNAL, vol. 18, p. 142-144, ISSN: 2226-6089
  • Turi A, LOGLISCI C, Salvemini E, Grillo G, Malerba D, D'Elia D (2008). Computational annotation of UTR cis-regulatory modules through Frequent Pattern Mining.. In: EMBnet Conference 2008. EMBNET NEWS, vol. 14, 3, ISSN: 1023-4144
  • ATTIMONELLI M, CASCIONE I, SANTAMARIA M, ACCETTURO M, LASCARO D, BERARDI M, CECI M, LOGLISCI C, MALERBA D (2005). A data mining approach to retrieve mitochondrial variability data associated to clinical phenotypes. In: Meeting of the Bioinformatic Italian Society, BITS 2005, Proceedings. Milan, Italy, March 2005

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.

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