Appel: IDEAS 2014, Data Preparation for Data Mining Track

[Thierry Hamon]

Date: Fri, 7 Mar 2014 10:55:49 +0000
From: Paulo Oliveira <pjo at isep.ipp.pt>
Message-Id: <8B843F68-FEF4-4096-A5C7-18FBB7F1E7DE at isep.ipp.pt>
X-url: http://confsys.encs.concordia.ca/IDEAS/ideas14/dataprepmine.php

(Please accept my apologies if you receive multiple copies of this CfP)

Data Preparation for Data Mining Track of the 
18th International Database Engineering & Applications Symposium 
(IDEAS 2014)
July 7-9, 2014, Porto, Portugal
http://confsys.encs.concordia.ca/IDEAS/ideas14/dataprepmine.php
 
(accepted papers will be included in the IDEAS14 proceedings and
published by ACM)
 
-----Call for Papers ---------

Current technological developments allow the collection of huge amounts
of data that can be used to support decision-making processes. However,
this is only possible if data can be transformed into knowledge.
 
Various kind of data mining algorithms are used to extract data
patterns.  Tasks for pattern extraction include classification (rules or
trees), regression, clustering, association, sequence modeling,
dependency, and so forth. However, much work in the field of data mining
was built on the existence of data with quality, and real-world data is
often incomplete, noisy, or inconsistent, representing an obstacle for
efficient data analysis/mining. Other challenges include big data
(number of features/examples, efficiency, parallel processing), curse of
dimensionality, or the use of domain knowledge. Although most mining
algorithms have some procedures for dealing with dirty data, they lack
for robustness. Furthermore, low-quality data will lead to low-quality
data analysis/mining results (Garbage in, garbage out). Data preparation
techniques, when applied before mining, can substantially improve the
overall quality of the data and consequently improve the mining results
and/or the time required for the actual mining process. Thus, the
development of data preparation techniques is both a challenging and a
critical task. This special session on Data Preparation for Data Mining
will address practical techniques and methodologies of data preparation
for data-mining applications.
 
Topics of Interest:
--------------------------
   - Data collecting
   - Data integration
   - Data reduction
   - Data cleaning
   - Detection of outliers
   - Data/Information quality
   - Data profiling
   - Data enrichment
   - Feature selection and transformation
   - Data summarization
   - Data discretization
   - Data encoding
   - Sampling
   - Data preparation on regression/classification
   - Data preparation on segmentation/clustering
   - Data preparation on association rules
   - Data preparation on text mining
   - Data preparation on web mining
   - Data preparation on visual data mining
   - Data preparation on temporal and spatial data mining
   - Data preparation on multimedia mining (audio/video)
 
Important Dates:
--------------------------
   - March 24, 2014: Papers submission deadline
   - May 19, 2014: Notification of acceptance
   - June 9, 2014: Camera-ready deadline
  
 Track Organizing Committee:
--------------------------
   - Pedro Henriques, University of Minho, Portugal
   - Fátima Rodrigues, Institute of Engineering - Polytechnic of Porto,
     Portugal
   - Paulo Oliveira, Institute of Engineering - Polytechnic of Porto,
     Portugal
   - Alberto Freitas, Faculty of Medicine- University of Porto, Portugal

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