Examining the Impact of Distance-Based Similarity Metrics on the Performance of Projected Clustering Algorithm for Fingerprint Database Clustering

The projected clustering (PROCLUS) algorithm is a subspace clustering algorithm based on the k-medoids clustering approach. It is designed to address the challenges of irrelevant received signal strength (RSS) measurements in fingerprint vectors by focusing on meaningful subsets of RSS measurements from wireless APs, known as subspaces. Despite its robustness, its performance heavily depends on the chosen similarity metric, with Euclidean and Manhattan distances being the most common. While many researchers focus on modifying the algorithm to enhance performance, the impact of similarity metrics on clustering performance is often overlooked, despite its critical role in determining accuracy. As such, this article evaluates the clustering performance of the PROCLUS algorithm using five distance-based similarity metrics—Euclidean, Manhattan, Cosine similarity, Canberra, and Chebyshev—across six experimentally generated fingerprint databases. It aims to identify the best similarity metric for maximizing clustering performance for each of the six fingerprint databases, with silhouette scores used as the clustering performance metric. Simulation results show that Cosine similarity is the most effective metric with the PROCLUS algorithm. It consistently produces clusters with the highest silhouette scores, all well above the 0.25 threshold and were 24% to 136% higher than the scores achieved using the other distance-based metrics across the tested fingerprint databases. The Canberra distance performed variably, while Euclidean and Manhattan distances were less reliable. The Chebyshev distance consistently underperformed in all the databases considered. The findings in this article highlight the importance of choosing the appropriate similarity metric to perform clustering operations with the PROCLUS algorithm.