Routine mining on location sequences

Abstract

In this article, we propose a novel routine pattern extraction architecture to analyze the daily behaviors of mobile users. The key component of the proposed architecture is a dynamic programming-based sequence dissimilarity calculation method, which aims to measure the dissimilarity between trajectories and then extract patterns using the clustering method. The method exploits three different information: (1) spatial-temporal information, (2) information in the continuous same location in the sequence and (3) the probabilities of location occurrences in the data. We conduct experiments on a synthetic dataset and two real-world datasets. The obtained results demonstrate that our proposed method is efficient in extracting hidden routine patterns from users’ trajectory data.