Call for papers:
Special Session on Data Mining and Knowledge Discovery (at FUSION 2014)

The International Conference on Information Fusion is the major venue for researchers and practitioners interested in the last advances of information fusion techniques and applications. FUSION 2014 (http://www.fusion2014.org/) will take place in Salamanca (Spain) on July 7-10 2014.

We are proposing a special session on Data Mining and Knowledge Discovery at FUSION 2014 to bring together researchers interested in the use of data mining techniques in information fusion problems. Submissions on topics related to theory and applications of data mining in the context of information fusion are welcome. The selected authors will present their contribution during the session, and their paper will be included in the conference proceedings published by IEEE.

If you are interested in submitting a contribution, please e-mail us the title of the paper, the names of the authors, and the name of the presenter by January 31 2014. Feel free to send questions or comments to the members of the committee.

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

Abstract
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 include, but are not limited to, the following:
• 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.

Paper submission
Papers must be submitted through the conference platform, where the name of the session will be specified. The length of the papers will be 6-8 pages including figures and references, and must follow the template specified at the conference web page (http://www.fusion2014.org/call-papers).
Before submitting, please send to the session organizers a statement of interest including authors, abstract and presenting author of your prospective contribution.

Important dates
• Statement of interest: 31 January 2014
• Paper submission deadline: 5 March 2014
• Notification of acceptance: 10 May 2014
• Camera-ready version: 26 May 2014
• Conference: 7-10 July 2014

Organizers
María José Martín-Bautista (mbautis@decsai.ugr.es), University of Granada
Daniel Sánchez (daniel@decsai.ugr.es), University of Granada
Juan Gómez-Romero (jgomez@decsai.ugr.es), University of Granada
M. Dolores Ruiz (mdruiz@decsai.ugr.es), University of Granada