Clustering Noisy Trajectories via Robust Deep Attention Auto-Encoders

Abstract

Trajectory clustering aims at grouping similar trajectories into one cluster. It is an efficient way of finding the representative path or common trend shared by different moving objects, and also provides a foundation for movement pattern mining, anomaly detection and other applications. Existing trajectory clustering studies mainly rely on feature selection and similarity measurement based on their geographical and spatial properties. However, one obstacle hindering their wide usage is the problem of clustering accuracy in the presence of noisy or incomplete sensing data, due to limited sensory device quantity, communication errors, sensor failures, and sensor vacancy. This paper proposes an error-tolerant trajectory clustering approach by incorporating denoising methods.We propose the Robust Deep Attention Auto-encoders model (called Robust DAA) to learn the representations of low-dimensional denoising trajectories with three novel features. First, we present the deep attention auto-encoders by integrating the attention mechanism into the classical deep auto-encoder, which is capable of enhancing feature propagation and feature selection. Second, we train the deep attention auto-encoder by applying proximal method, back propagation and the Alternating Direction of Method of Multipliers (ADMM). As a result, our Robust DAA can reduce the negative influence of the noise on trajectory data. Finally, we perform clustering over the low-dimensional denoising representations using traditional clustering algorithms and demonstrates the quality of the clustering results by comparing our approach with existing representative methods. Extensive experiments are conducted on both synthetic datasets and real datasets. The results show that our approach outperforms the existing models in terms of accuracy, precision, recall and f1-score.