Comparison Of K-Means, K-Medoids, and Fuzzy C-Means Algorithms for Clustering Drug User’s Addiction Levels

Abstract

Narcotics, psychotropics, and addictive substances are drugs that can activate brain systems, affect dopamine levels, and cause addiction. In Indonesia, there is a law requiring drug addicts to receive treatment and care. To properly treat a drug addict, it is first necessary to determine the level of addiction. Data mining methods such as clustering can be used to assess a user's level of drug addiction.  This study uses the clustering algorithms Fuzzy C-means, K-Medoids, and K-means. The performance of the three clustering algorithms will then be evaluated based on the average similarity of clusters. Data such as how many types of drugs that used, the length of time they were used, the psychiatric status, and the physical condition status, are used. Clustering was accomplished using the data mining software RStudio. The clustering algorithms were then evaluated with the Davies Bouldin Index (DBI). The K-Medoids algorithm was found to have the best average similarity value of cluster for determining drug users' addiction levels based on the results of the analysis.