A Brief Comparison of K-means and Agglomerative Hierarchical Clustering Algorithms on Small Datasets
Document Type
Conference Proceeding
Source of Publication
Lecture Notes in Electrical Engineering
Publication Date
1-1-2022
Abstract
In this work, the agglomerative hierarchical clustering and K-means clustering algorithms are implemented on small datasets. Considering that the selection of the similarity measure is a vital factor in data clustering, two measures are used in this study - cosine similarity measure and Euclidean distance - along with two evaluation metrics - entropy and purity - to assess the clustering quality. The datasets used in this work are taken from UCI machine learning depository. The experimental results indicate that k-means clustering outperformed hierarchical clustering in terms of entropy and purity using cosine similarity measure. However, hierarchical clustering outperformed k-means clustering using Euclidean distance. It is noted that performance of clustering algorithm is highly dependent on the similarity measure. Moreover, as the number of clusters gets reasonably increased, the clustering algorithms’ performance gets higher.
DOI Link
ISBN
9789811924552
ISSN
Publisher
Springer Nature Singapore
Volume
942 LNEE
First Page
623
Last Page
632
Disciplines
Computer Sciences
Keywords
Clustering, Clustering comparison, Cosine, Euclidean, Hierarchical clustering, K-means
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Abdalla, Hassan I., "A Brief Comparison of K-means and Agglomerative Hierarchical Clustering Algorithms on Small Datasets" (2022). All Works. 5203.
https://zuscholars.zu.ac.ae/works/5203
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Hybrid: This publication is openly available in a subscription-based journal/series