Crowdsourcing allows large-scale and flexible invocation of human input for data gathering and analysis, which introduces a new paradigm of data mining process. Traditional data mining methods often require the experts in analytic domains to annotate the data. However, it is expensive and usually takes a long time. Crowdsourcing enables the use of heterogeneous background knowledge from volunteers and distributes the annotation process to small portions of efforts from different contributions. This paper reviews the state-of-the-arts on the crowdsourcing for data mining in recent years. We first review the challenges and opportunities of data mining tasks using crowdsourcing, and summarize the framework of them. Then we highlight several exemplar works in each component of the framework, including question designing, data mining and quality control. Finally, we conclude the limitation of crowdsourcing for data mining and suggest related areas for future research.
Data miningCrowdsourcingQuality controlSurvey
Guo Xintong、Wang Hongzhi、Yangqiu Song、Gao Hong
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School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150006, China
Department of Computer Science and Technology, University of Illinois at Urbana-Champaign, IL, USA
NETL
NSTL
Elsevier
