Data Mining for the Internet of Things: Literature Review and Challenges

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  • Feng Chen
    Parallel Computing Laboratory, Institute of Software Chinese Academy of Sciences, Beijing 100190, China
  • Pan Deng
    Parallel Computing Laboratory, Institute of Software Chinese Academy of Sciences, Beijing 100190, China
  • Jiafu Wan
    School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China
  • Daqiang Zhang
    School of Software Engineering, Tongji University, Shanghai 201804, China
  • Athanasios V. Vasilakos
    Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 97187 Luleå, Sweden
  • Xiaohui Rong
    Chinese Academy of Civil Aviation Science and Technology, Beijing 100028, China

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<jats:p> 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. </jats:p>

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