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FUSION 2015 : SS Data Mining and Knowledge Discover - Fusion 2015


When Jul 6, 2015 - Jul 9, 2015
Where Washington D.C.
Submission Deadline Mar 1, 2015
Notification Due May 1, 2015
Final Version Due Jun 1, 2015

Call For Papers

The International Conference on Information Fusion is the major venue for researchers and practitioners interested in the latest advances in information fusion and their impacts on our society. FUSION 2015 ( will take place in Washington, D.C. on July 6-10 2015.

After a successful first edition in 2014, we are proposing a new edition of the special session on Data Mining and Knowledge Discovery in Information Fusion at FUSION 2015 to present the latest advances on the area. 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.

Please find below the complete call for papers. Feel free to send questions or comments to the members of the organizing committee.

Organizing committee
Maria J. Martin-Bautista (, University of Granada
Juan Gómez-Romero (, University of Granada
Daniel Sánchez (, University of Granada
M. Dolores Ruiz (, University of Granada


Special session on
Data Mining and Knowledge Discovery
in Information Fusion

Fusion 2015 (
July 6-9, Washington, D.C.

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 decision-making; and (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). Data mining and knowledge discovery methods can be used to extract from datasets elaborated knowledge that can be afterwards fused with sensor-based data and other information. Furthermore, data mining and knowledge discovery methods can be applied on fused data to achieve better inferences towards situation and threat assessment. Consequently, the Information Fusion community can benefit from well-established approaches and new advances in data mining and knowledge discovery –such as machine learning and pattern recognition algorithms, imprecise and uncertain knowledge management formalisms, big data analysis tools, natural language processing techniques, 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 the development and application of data mining and knowledge discovery methods in Information Fusion. The session is open to contributions generated by specialists in related areas in order to promote interdisciplinary collaborations and cross-fertilization.

Topics of interest include, but are not limited to, the following:

- Data mining from multiple sources
- Knowledge discovery for higher-level information fusion
- Fusion of data mining knowledge
- Stream data mining and temporal data series
- Big data mining
- Data, text and web mining in soft information fusion
- Imprecision, uncertainty and vagueness in data mining
- Anomaly and exception detection from datasets
- 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 extracted knowledge
- Applications: defense, surveillance, cyber-security, maritime and aerial traffic control, emergency management, etc.

Paper submission
Papers must be submitted through the system found at the conference submission system (, where the name of the session will be specified. The length of the papers should be 6-8 pages including figures and references, and must follow the template specified at the conference web page. See more details at

Before submitting their contributions, authors are encouraged to send to the session organizers a statement of interest including full author list, abstract and presenting author.

Related Resources

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ECML-PKDD 2017   European Conference on Machine Learning and Principles and Practice of Knowledge Discovery
MLDM 2017   Machine Learning and Data Mining in Pattern Recognition
DSAA 2017   The 4th IEEE International Conference on Data Science and Advanced Analytics 2017
MFI 2016   2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
IFLDS 2016   Special Issue on Information Fusion in Learning from Data Streams
CIKM 2017   The 26th 2017 ACM Conference on Information and Knowledge Management
ISMIS 2017   23rd International Symposium on Methodologies for Intelligent Systems
KDD 2017   Knowledge Discovery and Data Mining
FUSION 2016   19th International Conference on Information Fusion