Organised by
In Association with
Supported by

SS07 - Data Mining and Knowledge Discovery in Information Fusion

Data Mining aims at the automatic discovery of underlying non-trivial knowledge from datasets by applying intelligent analysis techniques. The interest in this research area has experienced a considerable growth in the last years due to two key factors: (a) knowledge hidden in organizations’ databases can be exploited to improve strategic and managerial decision-making; (b) the large volume of data managed by organizations makes it impossible to carry out a manual analysis. These issues are also becoming frequent in data and information fusion as a result of the increasing number of sensors used, the paradigm shift from lower-level object recognition to higher-level situation assessment, and the incorporation of heterogeneous sources to the fusion process (including soft information in textual form). Accordingly, the Information Fusion community can benefit from well-established approaches and new advances in data mining –such as machine learning, imprecise and uncertain knowledge management, big data analysis, pattern recognition, natural language processing, etc.– to develop fusion systems able to exploit more information sources more efficiently. The objective of this special session is to bring together researchers interested in data mining theories and applications relevant to information fusion. The session is open to contributions generated by researchers from related areas in order to promote interdisciplinary collaborations and cross-fertilization.

Topics of interest

  • Data, text and web mining
  • Stream data mining
  • Temporal data series
  • Big data mining
  • Imprecision, uncertainty and vagueness in data mining
  • Data pre- and post- processing
  • Parallel and distributed data mining algorithms        
  • Information summarization and visualization
  • Human-machine interaction for data access
  • Linguistic description of information
  • Semantic models to represent input data and extracted knowledge
  • Applications: defense, surveillance, maritime and aerial traffic control, anomaly detection, emergency management, situation recognition, etc.


Data mining, knowledge discovery, machine learning, big data analysis

Special Session Organizers

  • María José Martín-Bautista: University of Granada (Spain)
  • Daniel Sánchez: University of Granada (Spain)
  • Juan Gómez-Romero: University of Granada (Spain)
  • M. Dolores Ruiz: University of Granada (Spain)

Special Session Contact