A Comparative Agglomerative Hierarchical Clustering Method to Cluster Implemented Course

TL;DR

This paper compares different agglomerative hierarchical clustering methods, specifically single and complete linkage, to cluster courses implemented during industrial training, highlighting how different methods yield varying cluster counts.

Contribution

It introduces a comparative analysis of agglomerative hierarchical clustering techniques applied to course data using MATLAB and HCE 3.5.

Findings

Different clustering methods produce varying number of clusters.

Single linkage and complete linkage yield different cluster structures.

Clustering results depend on the chosen hierarchical approach.

Abstract

There are many clustering methods, such as hierarchical clustering method. Most of the approaches to the clustering of variables encountered in the literature are of hierarchical type. The great majority of hierarchical approaches to the clustering of variables are of agglomerative nature. The agglomerative hierarchical approach to clustering starts with each observation as its own cluster and then continually groups the observations into increasingly larger groups. Higher Learning Institution (HLI) provides training to introduce final-year students to the real working environment. In this research will use Euclidean single linkage and complete linkage. MATLAB and HCE 3.5 software will used to train data and cluster course implemented during industrial training. This study indicates that different method will create a different number of clusters.