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

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Listed:
  • Feng Chen
  • Pan Deng
  • Jiafu Wan
  • Daqiang Zhang
  • Athanasios V. Vasilakos
  • Xiaohui Rong

Abstract

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.

Suggested Citation

  • Feng Chen & Pan Deng & Jiafu Wan & Daqiang Zhang & Athanasios V. Vasilakos & Xiaohui Rong, 2015. "Data Mining for the Internet of Things: Literature Review and Challenges," International Journal of Distributed Sensor Networks, , vol. 11(8), pages 431047-4310, August.
    • Handle: RePEc:sae:intdis:v:11:y:2015:i:8:p:431047
    DOI: 10.1155/2015/431047
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