Overview of Agglomerative Hierarchical Clustering Methods.
Publication Date: 07/06/2024
Author(s): Oti Eric Uchenna, Michael O. Olusola.
Volume/Issue: Volume 7 , Issue 2 (2024)
Abstract:
Agglomerative hierarchical clustering methods are the most popular type of hierarchical clustering used to group objects in clusters based on their similarity. The methods uses a bottom-up approach and it starts clustering by treating the individual data points as a single cluster, then it is merged continuously based on similarity until it forms one big cluster containing all objects. In this paper, we reviewed eight agglomerative hierarchical clustering methods namely: single linkage method, complete linkage method, average linkage method, weighted group average method, centroid method, median method, Ward’s method and the flexible beta method; we also discussed measures of similarity and dissimilarity using quantitative data as our reference point.
Keywords:
Cluster, Dendrogram, Dissimilarity, Objects, Similarity.
