COMPUTATIONAL STATISTICS AND DATA ANALYSIS
CALL FOR PAPERS
Special Issue on
ADVANCES IN DATA MINING AND ROBUST STATISTICS
http://www.journals.elsevier.com/locate/csda
http://www.cmstatistics.org/ERCIM2013/docs/DMRobust.pdf
We are inviting submissions for a special issue of Computational
Statistics and Data Analysis focusing on Advances in Data Mining and
Robust Statistics.
The main aim of data mining is to extract knowledge from, usually very
large, datasets. Data mining techniques are often applied to gain
initial insights about the data and complement statistical
models. This special issue focuses on the interface between data
mining and statistical modelling, with special emphasis on robust
statistics. Very large datasets, especially those that are machine
generated and undergo limited quality control, are likely to contain
outliers and anomalous measurements. The analysis of such datasets
requires statistical approaches that are both computationally
efficient and robust against outliers and mild departures from model
assumptions. The broad scope includes, but is not limited to,
visualization techniques for very large and complex data, including
relational data, data analysis algorithms including optimisation and
search techniques, methodologies to draw inference on patterns and
subgroups, robust models, outlier detection methods, and the analysis
of dependencies.
The papers submitted to the special issue must have a computational
statistics or data analytic component in order to be considered for
publication. All submissions must contain original unpublished work
not being considered for publication elsewhere. Submissions will be
refereed according to standard procedures for Computational Statistics
and Data Analysis. Information about the journal can be found at
http://www.journals.elsevier.com/locate/csda.
The deadline for submissions is 17 January 2014. However, papers can
be submitted at any time and once they are received, they will enter
the editorial system immediately.
Papers for the special issue should be submitted using the Elsevier
Electronic Submission tool EES: http://ees.elsevier.com/csda. In the
EES, please choose the special issue on Data Mining and Robust
Statistics and the Co-Editor responsible for special issues.
The special issue editors:
Michael W. Berry, University of Tennessee, USA.
E-mail: [log in to unmask]
Jung Jin Lee, Soongsil University, Korea.
E-mail: [log in to unmask]
Giovanni Montana, Imperial College London, UK.
E-mail: [log in to unmask]
Stefan Van Aelst, Ghent University, Belgium.
E-mail: [log in to unmask]
Ruben H. Zamar, University of British Columbia, Canada.
E-mail: [log in to unmask]
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SIGNOFF allstat
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