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Feng Chen 1, 2 and Pan Deng 1 and Jiafu Wan 3 and Daqiang Zhang 4 and Athanasios V. Vasilakos 5 and Xiaohui Rong 6
Academic Editor:Houbing Song
1, Parallel Computing Laboratory, Institute of Software Chinese Academy of Sciences, Beijing 100190, China
2, Guiyang Academy of Information Technology, Guiyang 550000, China
3, School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China
4, School of Software Engineering, Tongji University, Shanghai 201804, China
5, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 97187 Luleå, Sweden
6, Chinese Academy of Civil Aviation Science and Technology, Beijing 100028, China
Received 17 January 2015; Accepted 1 March 2015; 30 August 2015
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1. Introduction
The Internet of Things (IoT) and its relevant technologies can seamlessly integrate classical networks with networked instruments and devices. IoT has been playing an essential role ever since it appeared, which covers from traditional equipment to general household objects [1] and has been attracting the attention of researchers from academia, industry, and government in recent years. There is a great vision that all things can be easily controlled and monitored, can be identified automatically by other things, can communicate with each other through internet, and can even make decisions by themselves [2]. In order to make IoT smarter, lots of analysis technologies are introduced into IoT; one of the most valuable technologies is data mining.
Data mining involves discovering novel, interesting, and potentially useful patterns from large data sets and applying algorithms to the extraction of hidden information. Many other terms are used for data mining, for example, knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, and information harvesting [3]. The objective of any data mining process is to build an efficient predictive or descriptive model of a large amount of data that not only best fits or explains it, but is also able to generalize to new data [4]. Based on a broad view of data mining functionality, data mining is the process of discovering interesting knowledge from large amounts of...