<div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div><br></div><div> ICCBR-14 Workshop<br></div><div><br></div><div> Workshop on Synergies between CBR and Data Mining</div><div>
<br></div><div> Call for Papers</div><div><br></div><div>At the core of CBR lies the ability of a system to learn from past cases. </div><div>However, CBR systems often incorporate data mining methods, for example, </div>
<div>to organize their memory or to learn adaptation rules. In turn, data </div><div>mining systems often utilize CBR as a learning methodology, for example, </div><div>through a common set of problems with the nearest-neighbor method and </div>
<div>reinforcement learning. Meanwhile, the machine learning community, </div><div>which is tightly coupled with data mining, has historically included CBR </div><div>among the types of instance-based learning.</div><div>
<br></div><div>This workshop will be dedicated to studying in-depth the possible </div><div>synergies between case-based reasoning (CBR) and data mining. It also </div><div>aims at identifying potentially fruitful ideas for co-operative </div>
<div>problem-solving where both CBR and data mining researchers can compare </div><div>and combine methods. In particular, new advances in data mining may help </div><div>CBR to advance its field of study and play a vital role in the future of </div>
<div>data mining. This first Workshop on Synergies between CBR and Data Mining </div><div>aims to:</div><div><br></div><div>* provide a forum for identifying important contributions and </div><div>opportunities for research on combining CBR and data mining, </div>
<div>* promote the systematic study of how to synergistically integrate CBR </div><div>and data mining, </div><div>* showcase synergistic systems using CBR and data mining. </div><div><br></div><div>Some of the technical issues addressed, and potential outcomes of the </div>
<div>workshop, are to identify the data mining methods used in CBR, to </div><div>categorize the problems addressed by data mining in CBR, to propose </div><div>methodological improvements to fit this context’s needs, preferred types </div>
<div>and methods, and guidelines to better develop CBR systems taking </div><div>advantage of all data mining research has to offer. Similarly, the </div><div>workshop will identify the CBR methods used in data mining, categorize </div>
<div>the problems addressed by CBR in data mining, propose methodological </div><div>improvements to fit this context’s needs, preferred types and methods, </div><div>and guidelines to better develop data mining systems taking advantage of </div>
<div>all CBR research has to offer.</div><div><br></div><div>We welcome all those interested in the problems and promise of </div><div>synergistically combining CBR and data mining whether they belong to the </div><div>CBR, the data mining community, or the machine learning community.</div>
<div><br></div><div>Topics of interest include (but are not limited to):</div><div><br></div><div>* Architectures for synergistic systems between CBR and data mining</div><div>* Theoretical frameworks for synergistic systems between CBR and data </div>
<div>mining</div><div>* Memory structure mining in CBR</div><div>* Memory organization mining in CBR (decision tree induction, etc.)</div><div>* Case mining</div><div>* Feature selection in CBR</div><div>* Knowledge discovery in CBR (adaptation knowledge, meta-knowledge, etc.)</div>
<div>* Concept mining in CBR</div><div>* Image and multimedia mining in CBR</div><div>* Temporal mining in CBR</div><div>* Text mining in CBR</div><div>* Nearest-neighbor systems and CBR</div><div>* Instance-based learning and CBR</div>
<div>* Reinforcement learning and CBR</div><div>* CBR and statistics</div><div>* CBR and statistical data analysis</div><div>* CBR in multi-strategy learning systems</div><div>* CBR and similarity and metric learning</div>
<div>* CBR and Big Data</div><div>* Application specific synergies between CBR and data mining (medicine, </div><div>bioinformatics, social networks, sentiment analysis, etc.)</div><div><br></div><div>Paper presentations will be interspersed with discussions in which we </div>
<div>characterize, categorize, and discuss the synergies between CBR and data </div><div>mining. A wrap-up round table discussion will summarize the lessons </div><div>learnt, issues identified, and future directions. </div>
<div><br></div><div>Submission Requirements</div><div><br></div><div>Submitted papers are limited to 10 pages in length. </div><div><br></div><div>All papers are to be submitted via the ICCBR-14 EasyChair system </div>
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(<a href="https://www.easychair.org/conferences/?conf=iccbr2014" target="_blank">https://www.easychair.org/conferences/?conf=iccbr2014</a>). </div><div>Papers should be in Springer LNCS format. Author's instructions, along </div>
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with LaTeX and Word macro files, are available at </div><div><a href="http://www.springer.de/comp/lncs/authors.html" target="_blank">http://www.springer.de/comp/lncs/authors.html</a>. </div><div><br></div><div>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.</div>
<div><br></div><div>Dates</div><div>* Submission Deadline: June 23, 2014</div><div>* Notification Date: August 12, 2014</div><div>* Camera-Ready Deadline: August 31, 2014</div><div>* Workshop date: September 29, 2014</div>
<div><br></div><div>Workshop Web Site: <a href="http://cs.oswego.edu/~bichinda/iccbr2014/" target="_blank">http://cs.oswego.edu/~bichinda/iccbr2014/</a></div><div><br></div><div>Organizing Committee</div><div><br></div><div>
Co-Chairs</div>
<div><br></div><div>Isabelle Bichindaritz</div><div>State University of New York, Oswego</div><div>Oswego, NY, 13126, USA</div><div>Phone: <a href="tel:%2B1%20315%20312%202683" value="+13153122683" target="_blank">+1 315 312 2683</a></div>
<div>Email: <a href="mailto:ibichind@oswego.edu" target="_blank">ibichind@oswego.edu</a></div>
<div><br></div><div>Cindy Marling</div><div>Ohio University </div><div>Athens, Ohio, 45701, USA</div><div>Phone: <a href="tel:%2B1%20740%20593%201246" value="+17405931246" target="_blank">+1 740 593 1246</a></div><div>Email: <a href="mailto:marling@ohio.edu" target="_blank">marling@ohio.edu</a></div>
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<div>Stefania Montani</div><div>University of Piemonte Orientale</div><div>I-15100 Alessandria, Italy </div><div>Phone: +30 0131 360158</div><div>Email: <a href="mailto:stefania.montani@unipmn.it" target="_blank">stefania.montani@unipmn.it</a></div>
<span class="HOEnZb"><font color="#888888">
<div><br></div></font></span></div></div><div><br></div>-- <br><div dir="ltr"><div> <span style="font-family:arial,helvetica,sans-serif"> Dr. Isabelle Bichindaritz</span></div><div><font face="arial, helvetica, sans-serif"> Assistant Professor</font></div>
<div><font face="arial, helvetica, sans-serif"> SUNY Oswego</font></div><div><font face="arial, helvetica, sans-serif"> Computer Science Department</font></div><div><font face="arial, helvetica, sans-serif"> Shineman 427</font></div>
<div><font face="arial, helvetica, sans-serif"><span style="color:rgb(34,34,34);line-height:17.77777862548828px;background-color:rgb(255,255,255)"> 7060 New York 104 Oswego, NY 13126</span></font></div><div><span style="color:rgb(34,34,34);line-height:17.77777862548828px;background-color:rgb(255,255,255)"><font face="arial, helvetica, sans-serif"> USA</font></span></div>
<div><font face="arial, helvetica, sans-serif"> Ph: (315) 312 2683</font></div><div><font face="arial, helvetica, sans-serif"> Email: <a href="mailto:ibichind@oswego.edu" target="_blank">ibichind@oswego.edu</a></font></div>
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