dc.contributor.authorShi, Ke
dc.description.abstractThe terrorist attack directly affects personal safety, and it also has a lasting impact on international politics, civil liberties, and the economy. Internet produces massive amounts of terrorist attack news every day, o how to extract news of interest is time-consuming work. In order to provide organized information to readers, clustering technology is used to automatically arrange vast news. In this project, a document representation model is trained by CNN and LSTM to represent each news as a 48-dimensional vector. Meanwhile, a hierarchical structure is designed to do the K-means and Affinity Propagation clustering. The first step is to cluster samples by locations, and the second step is to cluster samples by content information. As a result, the overall model obtains a satisfactory performance as Purity at 85.19%, RI at 82.12% and NMI at 76.42%.en_US
dc.format.extent75 p.en_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleEvent detection based on on-line news clusteringen_US
dc.contributor.supervisorMao Kezhien_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Computer Control and Automation)en_US

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