Unsupervised Deep Clustering for Human Behavior Understanding

We propose Compressed-Pseudo-Temporal Enhanced Representation Learning (C-PTER), a novel unsupervised clustering framework for human-centered behavior analysis. With the growing prevalence of wearables, smartphones, and IoT devices, vast amounts of human activity data are collected in real-world settings, yet traditional supervised learning approaches require extensive manual labeling, making them impractical for large-scale deployment. Existing deep clustering methods, such as autoencoder-based approaches, often fail to capture temporal dependencies and struggle with noisy sensor readings, leading to suboptimal clustering performance. In contrast, C-PTER integrates pseudo-temporal feature extraction with a parallel CNN-LSTM autoencoder, enabling robust spatial-temporal representation learning. By leveraging compressed feature extraction, our method enhances cluster compactness and inter-cluster separation, significantly improving clustering performance on real-world human activity datasets. We demonstrate that C-PTER outperforms both classical (k-means) and deep clustering baselines (DSC) across three Inertial Measurement Unit (IMU) benchmark datasets (MUser, UCI HAR, MHEALTH), achieving up to 30% improvement in normalized mutual information (NMI) and 21% in accuracy (ACC). These results validate C-PTER as a scalable and effective solution for clustering unsupervised human behavior.