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Data mining for the internet of things: Literature review and challenges
Parallel Computing Laboratory, Institute of Software Chinese Academy of Sciences, Beijing.
Parallel Computing Laboratory, Institute of Software Chinese Academy of Sciences, Beijing.
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou.
School of Software Engineering, Tongji University, Shanghai.
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2015 (English)In: International Journal of Distributed Sensor Networks, ISSN 1550-1329, E-ISSN 1550-1477, Vol. 2015, article id 431047Article in journal (Refereed) Published
Abstract [en]

The massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithms can be applied to IoT to extract hidden information from data. In this paper, we give a systematic way to review data mining in knowledge view, technique view, and application view, including classification, clustering, association analysis, time series analysis and outlier analysis. And the latest application cases are also surveyed. As more and more devices connected to IoT, large volume of data should be analyzed, the latest algorithms should be modified to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed

Place, publisher, year, edition, pages
2015. Vol. 2015, article id 431047
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-15945DOI: 10.1155/2015/431047ISI: 000361197700001Scopus ID: 2-s2.0-84941671076Local ID: f850c9b8-8f3d-4d75-8960-394f4f6a7ba2OAI: oai:DiVA.org:ltu-15945DiVA, id: diva2:988921
Note
Validerad; 2015; Nivå 2; 20150924 (andbra)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved

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Vasilakos, Athanasios

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