The Workshop on Synergies between CBR and Data Mining
will be held at the
International Conference on Case-Based Reasoning (ICCBR-14)
in Cork, Ireland, on September 29, 2014
Call for Papers
At the core of CBR lies the ability of a system to learn from past cases. However, CBR systems often incorporate data mining methods, for example, to organize their memory or to learn adaptation rules. In turn, data mining systems often utilize CBR as a learning methodology, for example, through a common set of problems with the nearest-neighbor method and reinforcement learning. Meanwhile, the machine learning community, which is tightly coupled with data mining, has historically included CBR among the types of instance-based learning.
This workshop will be dedicated to studying in-depth the possible synergies between case-based reasoning (CBR) and data mining. It also aims at identifying potentially fruitful ideas for co-operative problem-solving where both CBR and data mining researchers can compare and combine methods. In particular, new advances in data mining may help CBR to advance its field of study and play a vital role in the future of data mining. This first Workshop on Synergies between CBR and Data Mining aims to:
- provide a forum for identifying important contributions and opportunities for research on combining CBR and data mining,
- promote the systematic study of how to synergistically integrate CBR and data mining,
- showcase synergistic systems using CBR and data mining.
We welcome all those interested in the problems and promise of synergistically combining CBR and data mining whether they belong to the CBR, the data mining community, or the machine learning community.
Topics of interest include (but are not limited to):
- Architectures for synergistic systems between CBR and data mining
- Theoretical frameworks for synergistic systems between CBR and data mining
- Memory structure mining in CBR
- Memory organization mining in CBR (decision tree induction, etc.)
- Case mining
- Feature selection in CBR
- Knowledge discovery in CBR (adaptation knowledge, meta-knowledge, etc.)
- Concept mining in CBR
- Image and multimedia mining in CBR
- Temporal mining in CBR
- Text mining in CBR
- Nearest-neighbor systems and CBR
- Instance-based learning and CBR
- Reinforcement learning and CBR
- CBR and statistics
- CBR and statistical data analysis
- CBR in multi-strategy learning systems
- CBR and similarity and metric learning
- CBR and Big Data
- Application specific synergies between CBR and data mining (medicine, bioinformatics, social networks, sentiment analysis, etc.).
Paper presentations will be interspersed with discussions in which we characterize, categorize, and discuss the synergies between CBR and daya mining. a wrap-up round table discussion will summarize the lessons learnt, issues identifies, and future directions.
Submitted papers are limited to 10 pages in length. All papers are to be submitted via the CBRDM 2014 EasyChair system. Papers should be in Springer LNCS format. Author's instructions, along with LaTeX and Word macro files, are available at http://www.springer.de/comp/lncs/authors.html.
Submissions should be original papers that have not already been published elsewhere. However, papers may include previously published results that support a new theme, as long as all past publications are fully referenced.
- Submission Deadline:
July 4, 2014 extended to July 15, 2014
- Notification Date: August 8, 2014
- Camera-Ready Deadline: August 22, 2014
- Workshop Date: September 29, 2014
Workshop Web Site
Submission Web Site
State University of New York at Oswego, USA
Ohio University, USA
University of Piemonte Orientale, Italy
Agnar Aamodt, NTNU, Norway
Juan Manuel Cortado, University of Salamanca
Peter Funk, Malardalen University, Sweden
Jean Lieber, Loria, University of Nancy, France
Amedeo Napoli, Loria, University of Nancy, France
Lucia Sacchi, University of Pavia, Italy
Rainer Schmidt, Institute for Medical Informatics and Biometry, University of Rostock, Germany
We look forward to welcoming you to Cork!