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The Analysis of Proximity Between Subjects Based on Primary Contents Using Cosine Similarity on Lective

机译：基于主要内容的主语之间的接近度分析（基于Lective的余弦相似度）

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In education world, recognizing the relationship between one subject and another is imperative. By recognizing the relationship between courses, performing sustainability mapping between subjects can be easily performed.? Moreover, detecting and reducing any duplicated contents in several subjects will be also possible to execute. Of course, these conveniences will benefit lecturers, students and departments. It will ease the analysis and discussion processes between lecturers related to subjects in the same domain. In addition, students will conveniently choose a group of subjects they are interested in. Furthermore, departments can easily create a specialization group based on the similarity of the subjects and combine the courses possessing high similarity. In this research, given a good database, the relationship between subjects was calculated based on the proximity of the primary contents of the subjects. The feature used was term feature, in which value was determined by calculating TF-IDF (Term Frequency Inverse Document Frequency) from each term. In recognizing the value of proximity between subjects, cosine similarity method was implemented. Finally, testing was done utilizing precision, recall and accuracy method. The research results show that the precision and accuracy values are 90,91% and the recall value is 100%.
机译：在教育界，必须认识到一个学科与另一个学科之间的关系。通过认识课程之间的关系，可以轻松地执行科目之间的可持续性映射。此外，在几个主题中检测和减少任何重复的内容也将可能执行。当然，这些便利将使讲师，学生和部门受益。这将简化同一领域中与主题相关的讲师之间的分析和讨论过程。此外，学生可以方便地选择他们感兴趣的学科组。此外，各科室可以根据学科的相似性轻松创建一个专业化组，并将具有高度相似性的课程组合在一起。在这项研究中，给定一个良好的数据库，可以根据受试者主要内容的接近程度来计算受试者之间的关系。所使用的特征是术语特征，其中值是通过从每个术语计算TF-IDF（术语频率反文档频率）来确定的。为了认识对象之间的接近度，采用了余弦相似度方法。最后，使用精度，召回率和准确性方法进行测试。研究结果表明，查准率和查全率分别为90.91％和100％。

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