Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)

Methods of Collective Intelligence in Exploratory Data Analysis: A Research Survey

Authors
Piotr A. KOWALSKI, Szymon Lukasik, Piotr KULCZYCKI
Corresponding Author
Piotr A. KOWALSKI
Available Online December 2016.
DOI
https://doi.org/10.2991/cnct-16.2017.1How to use a DOI?
Keywords
Computational Intelligence, Collective Intelligence, Exploratory Data Analysis, Data Science, Classification, Clustering, Outlier Detection, Data and Dimensionality Reduction, Metaheuristics.
Abstract
This study contains a brief presentation of the basic tasks for Exploratory Data Analysis (EDA), namely: classification, clustering, reduction of data dimensionality and number of data instances as well as detection of outliers. Herein, solutions to the aforementioned problems incorporating a wide range of computational intelligence algorithms, in particular procedures based on collective intelligence, are under consideration. Furthermore, the combination of metaheuristic algorithms with basic EDA procedures applied and verified within many domains of science, technology and engineering are being presented.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)
Series
Advances in Computer Science Research
Publication Date
December 2016
ISBN
978-94-6252-301-2
ISSN
2352-538X
DOI
https://doi.org/10.2991/cnct-16.2017.1How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Piotr A. KOWALSKI
AU  - Szymon Lukasik
AU  - Piotr KULCZYCKI
PY  - 2016/12
DA  - 2016/12
TI  - Methods of Collective Intelligence in Exploratory Data Analysis: A Research Survey
BT  - Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)
PB  - Atlantis Press
SP  - 1
EP  - 7
SN  - 2352-538X
UR  - https://doi.org/10.2991/cnct-16.2017.1
DO  - https://doi.org/10.2991/cnct-16.2017.1
ID  - KOWALSKI2016/12
ER  -