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《Engineering Journal of Wuhan University》 2017-02
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LDA-K-means algorithm of network food safety topic detection

LIU Jinshuo;PENG Yingyue;ZHANG Lanxin;CHEN Yusen;DENG Juan;School of Computer Science,Wuhan University;International School of Software,Wuhan University;  
This paper presents an algorithm for the topic detection of food safety problems,which is using K-means clustering algorithm based on the latent dirichlet algorithm(LDA)model.The algorithm modeling the document space with LDA model,and select the probability distribution of the themes to the document as a vector of each document,process the vectors with K-means clustering algorithm,and finally get the results of the topic detection.In order to verify the effect of the test,this paper also carries out a set of traditional experiment as a control group,by using K-means algorithm based on VSM mode.Through the experiments on the data which covers 43 classes of 1920 news and Tencent micro blogging,we record the results of six experiments under different iterations and take the average.The experimental results show that the method proposed has a 20 percent increase than traditional methods on the three evaluation indices P,Rand F.
【Fund】: 国家自然科学基金项目(编号:61303214 61672393)
【CateGory Index】: TS201.6;TP391.1
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1 Niu Fenggao;Li Xing;School of Mathematical Sciences,Shanxi University;Institute of Management and Decision,Shanxi University;;Enhanced Considerations on Co-occurrence Latent Semantic Vector Space Model[J];情报杂志;2017-12
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