[Extended Deadline] CFP: Memetic Computing Journal special issue on Metaheuristics for Large Scale Data Mining

[Jaume Bacardit]

[Apologies if you receive multiple times this announcement]

Call for Papers: Memetic Computing Journal special issue on
Metaheuristics for Large Scale Data Mining - Extended Deadline

Guest editors:

Jaume Bacardit
School of Computer Science and School of Biosciences
University of Nottingham
jaume.bacardit@...

Xavier Llora
National Center for Supercomputing Applications
University of Illinois at Urbana-Champaign
xllora@...

Submission deadline: May 31st , 2009


Aim and Scope

Data mining and knowledge discovery are crucial techniques across many
scientific disciplines. Recent developments such as the Genome Project
(and its successors) or the construction of the Large Hadron Collider
have provided the scientific community with vast amounts of data.
Metaheuristics and other evolutionary algorithms have been successfully
applied to a large variety of data mining tasks. Competitive
metaheuristic approaches are able to deal with rule, tree and prototype
induction, neural networks synthesis, fuzzy logic learning, and kernel
machines -to mention but a few. Moreover, the inherent parallel nature
of some metaheuristics (e.g. evolutionary approaches, particle swarms,
ant colonies, etc) makes them perfect candidates for approaching very
large-scale data mining problems.

Although a number of recent techniques have applied these methods to
complex data mining domains, we are still far from having a deep and
principled understanding of how to scale them to datasets of terascale,
petascale or even larger scale. In order to achieve and maintain a
relevant role in large scale data mining, metaheuristics need, among
other features, to have the capacity of processing vast amounts of data
in a reasonable time frame, to use efficiently the unprecedented
computer power available nowadays due to advances in high performance
computing and to produce when possible- human understandable outputs.

Several research topics impinge on the applicability of metaheuristics
for data mining techniques: (1) proper scalable learning paradigms and
knowledge representations, (2) better understanding of the relationship
between the learning paradigms/representations and the nature of the
problems to be solved, (3) efficiency enhancement techniques, and (4)
visualization tools that expose as much insight as possible to the
domain experts based on the learned knowledge.

We would like to invite researchers to submit contributions on the area
of large-scale data mining using metaheuristics. Potentially viable
research themes are:

* Learning paradigms based on metaheuristics, evolutionary algorithms,
learning classifier systems, particle swarm, ant colonies, tabu search,
simulated annealing, etc
* Hybridization with other kinds of machine learning techniques
including exact and approximation algorithms
* Knowledge representations for large-scale data mining
* Advanced techniques for enhanced prediction (classification,
regression/function approximation, clustering, etc.) when dealing with
large data sets
* Efficiency enhancement techniques
* Parallelization techniques
* Hardware acceleration techniques (vectorial instuctions, GPUs, etc.)
* Theoretical models of the scalability limits of the learning
paradigms/representations
* Principled methodologies for experiment design (choosing methods,
adjusting parameters, etc.)
* Explanatory power and visualization of generated solutions
* Data complexity analysis and measures
* Ensemble methods
* Online data mining and data streams
* Examples of real-world successful applications


Instructions for authors

Papers should have approximately 20 pages (but certainly not more than
24 pages). The papers must follow the format of the Memetic Computing
journal:
http://www.springer.com/engineering/journal/12293?detailsPage=contentItemPage&CIPageCounter=151543 


Papers should be submitted following the Memetic Computing journal
guidelines. When submitting the paper please select this special issue
as the article type.


Important dates

Manuscript submission: May 31st, 2009

Notification of acceptance: July 31st, 2009

Submission of camera-ready version: Sep 30th, 2009





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Jaume Bacardit, PhD
Lecturer in Bioinformatics
University of Nottingham

Automated Scheduling, Planning and Optimisation research group,
School of Computer Science, Jubilee Campus, Nottingham, NG8 1BB, UK

Multidisciplinary Centre for Integrative Biology,
School of Biosciences, Sutton Bonington, LE12 5RD, UK

Tel: [phone]
Fax: [phone]
Email: jaume _dot_ bacardit _at_ nottingham _dot_ ac _dot_ uk
Web: http://www.cs.nott.ac.uk/~jqb
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-------------------------------------------------------------------
Jaume Bacardit, PhD
Lecturer in Bioinformatics
University of Nottingham

Automated Scheduling, Planning and Optimisation research group,
School of Computer Science, Jubilee Campus, Nottingham, NG8 1BB, UK

Multidisciplinary Centre for Integrative Biology,
School of Biosciences, Sutton Bonington, LE12 5RD, UK

Tel: [phone]
Fax: [phone]
Email: jaume _dot_ bacardit _at_ nottingham _dot_ ac _dot_ uk
Web: http://www.cs.nott.ac.uk/~jqb
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