| 改进K-means算法在高校舆情中的应用 |
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| 引用本文： | 徐建国,韩琮师.改进K-means算法在高校舆情中的应用[J].教育技术导刊,2019,18(7):142-144. |
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| 作者姓名： | 徐建国 韩琮师 |
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| 作者单位： | 山东科技大学，计算机科学与工程学院，山东 青岛 266590 |
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| 基金项目： | 国家重点研发计划项目（2017YFC0804406） |
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| 摘 要： | 互联网时代，网络焦点话题讨论对当代高校学生的思想有很大影响，因此对高校舆情进行监测具有十分重要的意义。通过改进的K-means算法对高校舆情进行聚类，获取舆情热点。通过聚类算法获取热点话题，进而对热点舆情话题进行引导，对改进高校学生思想政治工作作用显著。对改进算法进行实验，结果表明该算法准确率达到75%，比传统算法高出8%，改善了传统算法的聚类效果。
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| 关 键 词： | 高校舆情 聚类 K-means算法 |
| 收稿时间： | 2019-05-20 |
Application of Improved K-means Algorithm in University Public Opinion |
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| XU Jian-guo,HAN Cong-shi.Application of Improved K-means Algorithm in University Public Opinion[J].Introduction of Educational Technology,2019,18(7):142-144. |
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| Authors: | XU Jian-guo HAN Cong-shi |
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| Institution: | College of Computer Science and Engineering，Shandong University of Science and Technology，Qingdao 266590，China |
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| Abstract: | In the Internet age， the discussion of network focus topics has a great influence on the thinking of contemporary college students. Therefore， it is of great significance to monitor public opinion in colleges and universities. Through the improved K-means algorithm， the college public opinion clusters， the hotspots and the hot topics of the current colleges and universities are obtained through the clustering algorithm， which can guide the hot topic of the hot topics and play an important role in the development of college students' thoughts. Experiments on the improved algorithm show that the accuracy of the algorithm reaches 75%， which is 8% higher than the traditional algorithm， which improves the clustering effect of the traditional algorithm. |
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| Keywords: | university public opinion clustering K-means algorithm |
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