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

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.