基于DBSCAN算法与句间关系的热点话题发现研究

发布时间：2018-03-18 05:15

  本文选题：信息用户　切入点：热点话题　出处：《图书情报工作》2017年12期 　论文类型：期刊论文

【摘要】：[目的 /意义]在大数据时代面对海量的数据用户有时会束手无策。因此,越来越多的学者们开始关注互联网热点话题发现的算法,帮助用户快速获取热点话题。[方法 /过程]基于DBSCAN算法,通过动态调整参数来优化算法,实现热点话题发现。根据句法结构与句间关系分析构建热点话题过滤模型,过滤包含热点词项的一般话题。[结果 /结论]采用主流网站新闻数据集进行实验,利用错检率、漏检率等评价指标对算法的有效性进行检验,实验结果证明改进算法性能有所提升,能够为信息用户提供科学研究网络数据的高效途径。
[Abstract]:[purpose / significance] in big data's time faced with massive data users will sometimes be helpless. Therefore, more and more scholars are beginning to pay attention to the Internet hot topic discovery algorithm, [methods / procedures] based on DBSCAN algorithm, the algorithm is optimized by dynamically adjusting parameters to realize hot topic discovery. Based on the analysis of syntactic structure and sentence relationship, a hot topic filtering model is constructed. Filtering general topics containing hot words. [results / conclusions] using mainstream website news data set to test the effectiveness of the algorithm, using error detection rate, missed detection rate and other evaluation indicators, The experimental results show that the improved algorithm can improve the performance of the algorithm and provide an efficient way for information users to study network data scientifically.
【作者单位】： 长春理工大学图书馆;长春市农业信息中心;
【分类号】：G254
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本文编号：1628151