CFP WK on Synergies between CBR and Data Mining @ ICCBR - Deadline 6/23/2014

[Isabelle Bichindaritz]

                               ICCBR-14 Workshop

         Workshop on Synergies between CBR and Data Mining

                                   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.

Some of the technical issues addressed, and potential outcomes of the
workshop, are to identify the data mining methods used in CBR, to
categorize the problems addressed by data mining in CBR, to propose
methodological improvements to fit this context’s needs, preferred types
and methods, and guidelines to better develop CBR systems taking
advantage of all data mining research has to offer. Similarly, the
workshop will identify the CBR methods used in data mining, categorize
the problems addressed by CBR in data mining, propose methodological
improvements to fit this context’s needs, preferred types and methods,
and guidelines to better develop data mining systems taking advantage of
all CBR research has to offer.

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 data
mining.  A wrap-up round table discussion will summarize the lessons
learnt, issues identified, and future directions.

Submission Requirements

Submitted  papers are limited to 10 pages in length.

All papers are to be submitted via the ICCBR-14 EasyChair system
(https://www.easychair.org/conferences/?conf=iccbr2014).
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.

Dates
* Submission Deadline: June 23, 2014
* Notification Date:  August 12, 2014
* Camera-Ready Deadline:  August 31, 2014
* Workshop date:  September 29, 2014

Workshop Web Site: http://cs.oswego.edu/~bichinda/iccbr2014/

Organizing Committee

Co-Chairs

Isabelle Bichindaritz
State University of New York, Oswego
Oswego, NY, 13126, USA
Phone: +1 315 312 2683
Email: ibichind at oswego.edu

Cindy Marling
Ohio University
Athens, Ohio, 45701, USA
Phone: +1 740 593 1246
Email: marling at ohio.edu

Stefania Montani
University of Piemonte Orientale
I-15100 Alessandria, Italy
Phone: +30 0131 360158
Email: stefania.montani at unipmn.it


-- 
                 Dr. Isabelle Bichindaritz
                    Assistant Professor
                       SUNY Oswego
            Computer Science Department
                       Shineman 427
      7060 New York 104  Oswego, NY 13126
                               USA
                      Ph: (315) 312 2683
               Email: ibichind at oswego.edu
-------------- nächster Teil --------------
Ein Dateianhang mit HTML-Daten wurde abgetrennt...
URL: <https://lists.tu-clausthal.de/cgi-bin/mailman/private/ifi-ci-event/attachments/20140602/d93b9eda/attachment.html>