Characteristics-Based Least Common Multiple: A Novel Clustering Algorithm to Optimize Indoor Positioning

Hamaad Rafique, Davide Patti, Maurizio Palesi, Gaetano Carmelo La Delfa

2024

Abstract

Clustering is an unsupervised learning technique for grouping data based on similarity criteria. Conventional clustering algorithms like K-Means and agglomerative clustering often require predefined parameters such as the number of clusters and struggle to identify irregularly shaped clusters. Additionally, these methods fail to correctly cluster magnetic field signals with similar characteristics used for positioning in magnetic fingerprint-based indoor localization. This paper introduces a novel Characteristics-Based Least Common Multiple (LCM) clustering algorithm to address these limitations. This algorithm autonomously determines the number and shape of clusters and correctly classifies misclassified points based on characteristic similarities using LCM-based techniques. The effectiveness of the proposed technique was evaluated using state-of-the-art metrics like the Silhouette score, Calinski-Harabasz Index, and Davies-Bouldin Index on benchmark datasets.

Paper Citation

in Harvard Style

Rafique H., Patti D., Palesi M. and La Delfa G. (2024). Characteristics-Based Least Common Multiple: A Novel Clustering Algorithm to Optimize Indoor Positioning. In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-717-7, SciTePress, pages 301-308. DOI: 10.5220/0012943900003822

in Bibtex Style

@conference{icinco24,
author={Hamaad Rafique and Davide Patti and Maurizio Palesi and Gaetano Carmelo La Delfa},
title={Characteristics-Based Least Common Multiple: A Novel Clustering Algorithm to Optimize Indoor Positioning},
booktitle={Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2024},
pages={301-308},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012943900003822},
isbn={978-989-758-717-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Characteristics-Based Least Common Multiple: A Novel Clustering Algorithm to Optimize Indoor Positioning
SN - 978-989-758-717-7
AU - Rafique H.
AU - Patti D.
AU - Palesi M.
AU - La Delfa G.
PY - 2024
SP - 301
EP - 308
DO - 10.5220/0012943900003822
PB - SciTePress