A feature extraction algorithm for missing trajectory data clustering

Abstract

Feature extraction is one of the critical technologies in trajectory data clustering. Extracting useful features for trajectory clustering is a difficult problem when there are missing segments in trajectories. This paper proposes a feature extraction algorithm for missing trajectories clustering. The algorithm converts trajectories into images, a multi-layer image preprocessing method is proposed to reduce the information loss during image processing. Then build an autoencoder to extract image features. The loss function is designed according to the attention mechanism to highlight the effective information in the trajectory image. The autoencoder’s ability to handle missing trajectory segments is trained by adding artificial image masking. The effect of feature extraction is verified by clustering. Compared with the unimproved autoencoding and interpolation methods, the clustering effect of the features extracted by this algorithm is improved. At missing rate 50%, this is an increase of eight and six percentage points, respectively. It is proved that the algorithm in this paper is more suitable for missing trajectory feature extraction.