Data Mining for the Internet of Things: Literature Review and Challenges. (30th August 2015)

• Record Type: Journal Article
• Authors: Chen, Feng; Deng, Pan; Wan, Jiafu; Zhang, Daqiang; Vasilakos, Athanasios V.; Rong, Xiaohui
• Other Names: Song Houbing Academic Editor.
• Is Part Of: International journal of distributed sensor networks. (2015)
• Publication Date: 2015-08-30
• Subjects: Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1155/2015/431047
• Languages: English
• ISSNs: 1550-1329

Abstract

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.