Application of SKATER and Ward’s methods in grouping Indonesian provinces based on monthly expenditure per capita of food commodity groups

Abstract

Clustering is a fundamental data mining instrument that intends to find inherent cluster structure in data. Spatial clustering methods are usually used to assess the demographic data characterization. This study aims to classify provinces in Indonesia based on monthly expenditure per capita according to food commodity groups by using Ward’s and Spatial ‘K’luster analysis by tree edge removal (SKATER) methods and to identify a better classification between the two methods. The variables of this research constitute percentages of expenditure per capita for 14 groups of food commodities of 34 provinces in Indonesia during March 2018. The results of the first analysis (excluding outliers) revealed that SKATER method produced standard deviation rasio of 0.236, better than Ward’s method that produced standard deviation rasio of 0.370. However, from the second analysis (including outliers), the outcomes showed that the Ward’s method generated standard deviation rasio of 0.170, better than SKATER method that delivered standard deviation rasio of 0.199. Moreover, it can be concluded that the second analysis is better than the first analysis because it produced smaller standard deviation ratios based on the Ward’s and SKATER methods contrasted with the first one.