Data Preparation for Data Mining Track of
the
18th International Database Engineering &
Applications Symposium (IDEAS 2014)
July 7-9, 2014, Porto, Portugal
(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