Application of Cluster Analysis Using Agglomerative Method

Muhammad Rais Ridwan, H. Retnawati
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引用次数: 1

Abstract

Improving the quality of human resources is the main supporting factor in increasing national productivity in various fields and development sectors. The government's productive investment activities that spur the nation's competitiveness in the global era prioritize Indonesia's education development. This study aims to cluster provinces in Indonesia based on educational indicators using the Agglomerative method consisting of the Average Linkage and Ward methods. Data collection is based on documentation techniques obtained from Statistics Indonesia in 2018. Data analysis used hierarchical cluster analysis consisting of data standardization, determining the size of the similarity or dissimilarity between data, the clustering process with a distance matrix, and seeing the characteristics of the cluster results formed. The second clustering method is by doing the initial grouping and determining the excellent cluster based on the average standard deviation ratio to the standard deviation between groups. Clustering results show the Ward method with the number of collections as many as 4 clusters and produces a ratio with a value of 0.01 smaller than the Average Linkage method. It shows that the cluster analysis method using the Ward method has better group accuracy quality than the Average Linkage method.
凝聚法在聚类分析中的应用
提高人力资源质量是提高各领域和发展部门国民生产力的主要支撑因素。政府的生产性投资活动促进了国家在全球时代的竞争力,优先考虑了印尼的教育发展。本研究旨在使用由平均联系和沃德方法组成的聚集方法,根据教育指标对印度尼西亚的省份进行聚类。数据收集基于2018年从印尼统计局获得的文件技术。数据分析采用了分层聚类分析,包括数据标准化、确定数据之间相似性或不相似性的大小、用距离矩阵进行聚类处理,以及看到聚类结果的特征形成。第二种聚类方法是进行初始分组,并根据组间标准差的平均标准差比确定优秀聚类。聚类结果显示Ward方法具有多达4个聚类的集合数量,并且产生的比值比Average Linkage方法小0.01。结果表明,使用Ward方法的聚类分析方法比平均连锁方法具有更好的群精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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