Density-based spatial clustering of applications with noise using Gower distance

Abstract

Most clustering algorithms considering spatial characteristics of data have been developed based on the geological location of observations. Density-based spatial clustering of applications with noise (DBSCAN) provides arbitrarily shaped clusters grouping a set of observations which are closely packed together and noise detecting outliers which lie alone in low-density regions. A distance measure for DBSCAN is Euclidean distance, which is the standard measure of distance and especially suitable to handle continuous variables. To handle both categorical and continuous variables simultaneously, other measures are required to compute distance for various types of variables. Thus, we propose DBSCAN algorithm using Gower distance. We provide numerical results on spatial and non-spatial setup comparing DBSCAN methods with Euclidean and Gower distance and we apply this method to land price data and migraine treatments data. DBSCAN using Gower distance has a reasonable method and gives comparably stable results.