Analysis of Multivariate Indoor Building Data: A Comparative Study of Time-Series Clustering Methods
Year: 2025
Type: article
Abstract: Clustering multivariate time-series data is crucial for uncovering complex temporal patterns in dynamic environments, such as building indoor conditions and behavior where variables like temperature, humidity, and CO2concentration evolve simultaneously. This study conducts a comparison of the performance of six clustering techniques: KMeans with Euclidean and Dynamic Time-Warping (DTW) distances, HDBSCAN, KShapes, Self-Organizing Maps (SOM), and Bi-Clustering combined with different normalization methods, applied to real-world indoor sensor datasets. Results show that applying a first-order derivative transformation notably improved clustering quality across all methods by highlighting temporal dynamics. Bi-Clustering and KMeans (Euclidean) consistently outperformed others, achieving higher Silhouette scores, Calinski-Harabasz indices, and lower Davies-Bouldin scores. These findings underscore the necessity of multivariate time-series clustering in capturing latent dependencies across variables and time-relevant data that are often missed in univariate analyses. Moreover, incorporating derivative-based preprocessing enhances the ability of clustering algorithms to distinguish subtle temporal dynamics, making this approach highly relevant for applications in smart building monitoring and environmental control.
Source: IEEE Access
Authors: Quoc-Dung Ngo, Ly-Huynh Phan
Institution: Posts and Telecommunications Institute of Technology
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4413017610