FCM using squared euclidean distance for e-commerce classification in Indonesia

Abstract

Clustering is a method of grouping data into several clusters so that the data in one cluster has a high level of similarity while the data between other clusters have a low level of similarity. One method used in clustering is Fuzzy C-Means (FCM) which is a data clustering technique in which the existence of each data point in a cluster is determined by the degree of membership in each cluster. The FCM algorithm has an objective function that requires distance. The distance used in this study is Squared Euclidean distance. The clustering conducted is the clustering of the popularity of e-commerce in Indonesia in 2019 using the variable average number of monthly visitors, number of website visitors, number of social media followers (Twitter, Instagram, and Facebook) as well as the number of workers. The result of this method is the level of popularity of e-commerce in Indonesia, which is divided into gold, silver, and bronze. Clustering results were tested with the Partition Entropy Index (PEI) and Classification Entropy (CE) if the results are getting closer to 0, the results are getting better. The result of PEI is 2.9697e-0, and CE is 2.5710e-04. So, based on the two indexes It can be concluded that FCM using Squared Euclidean distance is good to clustering.