Proceedings of 2013 International Conference on Information Science and Computer Applications

Data Mining in Cloud Computing

Authors
Xia Geng, Zhi Yang
Corresponding Author
Xia Geng
Available Online October 2013.
DOI
https://doi.org/10.2991/isca-13.2013.1How to use a DOI?
Keywords
Data Mining, Cloud Computing, Map-Reduce, Hadoop
Abstract
Mining is a process of extracting potentially useful information from raw Data, so as to improve the quality of the information service. With the rapid development of the Internet, the size of the data has increased from KB level to TB even PB level; The object of data mining is also more and more complicated, so the data mining algorithm need to be more efficient. Cloud computing can provide infrastructure to massive and complex data of data mining, as well as new challenging issues for data mining of cloud computing research are emerged. This paper introduces the basic concept of cloud computing and data mining firstly, and sketches out how data mining is used in cloud computing; Then summarizes the research of parallel programming mode especially analyses the Map-reduce programming model and it's development platform-Hadoop; finally, overviews efficient mass data mining algorithm based on parallel programming model and mass data mining service based on the cloud computing.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2013 International Conference on Information Science and Computer Applications (ISCA 2013)
Part of series
Advances in Intelligent Systems Research
Publication Date
October 2013
ISBN
978-90786-77-85-7
ISSN
1951-6851
DOI
https://doi.org/10.2991/isca-13.2013.1How 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  - Xia Geng
AU  - Zhi Yang
PY  - 2013/10
DA  - 2013/10
TI  - Data Mining in Cloud Computing
BT  - 2013 International Conference on Information Science and Computer Applications (ISCA 2013)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/isca-13.2013.1
DO  - https://doi.org/10.2991/isca-13.2013.1
ID  - Geng2013/10
ER  -