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

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 f ingerprint-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 similari ties 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.