首页> 外文OA文献 >Determining The Senior High School Major of Students SMPN 2 Purwodadi Using Agglomerative Hierarchial Clustering Algorithm
【2h】

Determining The Senior High School Major of Students SMPN 2 Purwodadi Using Agglomerative Hierarchial Clustering Algorithm

机译:聚集层次聚类算法确定学生SMPN 2 Purwodadi的高中专业

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

Selection of majors by the students according to interests when enrolling in high school has the aim to provide opportunities for students to develop competence attitudes and competence skills of students according with their interests, talents, and academic skills in a group of subjects in science. Determination of the majors will affect the next academic level and will affect the field of science or studies for students who will continue on to the university level so that the selection of incorrect majors could harm students and their future. Clustering is one known technique in data mining, core understanding of clustering in data mining is a grouping of data or number of objects into groups so that each of the clustering will contain the data that is as similar as possible and the object different from other clusters. There are two methods of clustering that we know which is hierarchical clustering and partitioning. Hierarchical clustering method consists of a complete linkage clustering, single linkage clustering, average linkage clustering and centroid linkage clustering. While the method of partitioning itself consists of k-means clustering and k-means clustering fuzy. Methods will be tried hierarchy applied in this study is the fifth hierarchical clustering method previously mentioned. This method will be compared five weeks to see what works cluster grouping is expected to be known to the similarity or proximity between data that can be grouped into clusters, where among the cluster members have a high level of similarity. This study will analyze the application of hiearachial clustering techniques for grouping students of SMP Negeri 2 Purwodadi in choosing majors when will enroll in senior high school. In this research, the implementation of Agglomerative Hierarchial Clustering for classification prediction to selecting the major in senior high school get the fair result. From this research result, system prototyping is developed for the visualization that can help student to determine major in senior high school.
机译:入读高中时,学生可根据兴趣选择专业,目的是为学生提供机会,以根据他们在科学学科中的兴趣,才华和学术技能发展学生的能力态度和能力。确定专业将影响下一学年的水平,并将影响将继续升入大学水平的学生的科学或研究领域,因此选择错误的专业可能会损害学生及其未来。聚类是数据挖掘中的一种已知技术,对数据挖掘中的聚类的核心理解是将数据或对象的数量分为几组,以便每个聚类将包含尽可能相似的数据,并且对象与其他聚类不同。我们知道有两种聚类方法,即分层聚类和分区。层次聚类方法包括一个完整的链接聚类,单个链接聚类,平均链接聚类和质心链接聚类。分区方法本身包括k-均值聚类和k-均值聚类。将被尝试的方法应用于本研究中的层次结构是前面提到的第五种层次聚类方法。将对该方法进行五周比较,以了解可以分组的数据之间的相似性或相近性对群集分组的工作原理有所了解,而群集成员之间的相似度很高。这项研究将分析Hiearachial聚类技术在将SMP Negeri 2 Purwodadi的学生分组入读高中时的选择中的应用。在本研究中,采用聚类层次聚类进行分类预测以选择高中专业,取得了较好的效果。根据这项研究结果,开发了用于可视化的系统原型,可以帮助学生确定高中的专业。

著录项

  • 作者

    MAHENDRA HARUM ARISTA;

  • 作者单位
  • 年度 2017
  • 总页数
  • 原文格式 PDF
  • 正文语种
  • 中图分类

相似文献

  • 外文文献
  • 中文文献
  • 专利

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号