---

Author(s): Oti Eric Uchenna, Michael O. Olusola.

Volume/Issue: Volume 7, Issue 2 (2024)

Page No: 14-23

Journal: British Journal of Computer, Networking and Information Technology (BJCNIT)

---

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.

No. of Downloads: 0