Agglomerative Nesting (AGNES) Method and Divisive Analysis (DIANA) Method For Hierarchical Clustering On Some Distance Measurement Concepts

IF 1.1 Q3 STATISTICS & PROBABILITY
Susi Wijuniamurti, S. Nugroho, R. Rachmawati
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引用次数: 0

Abstract

Clustering data through hierarchical approach could be performed by Agglomerative Nesting (AGNES) Method and Divisive Analysis (DIANA) Method. The objective of this research is to compare both the methods based on Euclid and Manhattan distance measurements. Of this research the clustering procedures of agglomerative method are conducted by exploring all techniques including single linkage, complete linkage, average linkage, and Ward. The data used are the National Socio-Economic Survey (SUSENAS) data which are selected specifically for the percentage of over 5 year old residents in each province, for both living in urban or rural, who access the internet in the last 3 months in 2017 but classified according purpose of accessing. By applying Mean Square Error (MSE) for 2 and 3 clusters, it can be concluded that the single linkage technique is the best performance of clustering procedure for both Euclidean and Manhattan distances.
在一些距离测量概念上的聚类方法的凝聚嵌套法和分裂分析法
分层聚类数据可采用聚类嵌套法(AGNES)和分裂分析法(DIANA)进行。本研究的目的是比较基于欧几里得和曼哈顿距离测量的两种方法。在本研究中,通过探索单链接、完全链接、平均链接和Ward等所有技术,进行了聚类方法的聚类过程。使用的数据是国家社会经济调查(SUSENAS)数据,这些数据是专门为2017年最后3个月访问互联网的各省5岁以上城市或农村居民的百分比选择的,但根据访问目的进行分类。通过对2类和3类聚类的均方误差(MSE)分析,得出单链接聚类技术在欧氏距离和曼哈顿距离的聚类过程中性能最好的结论。
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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来源期刊
CiteScore
2.00
自引率
15.40%
发文量
42
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