A Study on the Effect of Distance Metrics on the Performance of Clustering Algorithms

Abstract

Clustering algorithms mainly use Euclidean distance as a distance metric for calculating the similarity between patterns. However, there are various distance metrics other than Euclidean distance, and a suitable distance metric for clustering is expected to be different depending on the characteristics of data. In this paper, we perform clustering algorithms on several datasets with various metrics and examine the effect of distance metrics on their performance.