Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)

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A Comparative Analysis of DBSCAN, K-Means and Agglomerative Clustering Algorithms for Geospatial Data

Authors
Anupam Jain1, *, Khushal Rathi1, Yuboraj Ganguly1, Ankit Kumar1, Yogiraj Bhale1
1AIT CSE, Chandigarh University, Punjab, 140301, India
*Corresponding author. Email: anupamayushij@gmail.com
Corresponding Author
Anupam Jain
Available Online 26 May 2025.
DOI
10.2991/978-94-6463-716-8_18How to use a DOI?
Keywords
Density-Based Spatial Clustering of Applications with Noise (DBSCAN); Non-negative Matrix Factorization (NMF); Singular Value Decomposit; Root Mean Square Error (RMSE); Mean Absolute Error (MAE); Systematic Literature Review (SLR); k-Nearest Neighbors (k-NN); Deep Neural Network (DNN); Multilayer Perceptron (MLP)
Abstract

This study presents a comparative analysis of three popular clustering algorithms, DBSCAN and KMeans, Agglomerative Clustering applied to geospatial data. We focus on their performance based on the silhouette score, examining their ability to identify meaningful clusters in noisy data. Our results show that DBSCAN outperforms KMeans and Agglomerative 9oClustering, achieving a silhouette score of 0.8646 compared to KMeans’ 0.8160 and Agglomerative Clustering’s 0.8160, highlighting DBSCAN’s robustness in identifying clusters with irregular shapes and handling noise.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
Series
Advances in Intelligent Systems Research
Publication Date
26 May 2025
ISBN
978-94-6463-716-8
ISSN
1951-6851
DOI
10.2991/978-94-6463-716-8_18How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Anupam Jain
AU  - Khushal Rathi
AU  - Yuboraj Ganguly
AU  - Ankit Kumar
AU  - Yogiraj Bhale
PY  - 2025
DA  - 2025/05/26
TI  - A Comparative Analysis of DBSCAN, K-Means and Agglomerative Clustering Algorithms for Geospatial Data
BT  - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
PB  - Atlantis Press
SP  - 212
EP  - 221
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-716-8_18
DO  - 10.2991/978-94-6463-716-8_18
ID  - Jain2025
ER  -