Title: Clustering-based multidimensional sequential pattern mining of semantic trajectories

Authors: Thouraya Sakouhi; Jalel Akaichi

Addresses: Institut Supérieur de Gestion de Tunis, Université de Tunis, LR99ES04 BESTMOD, Tunis, Tunisia; Esprit School of Business, Ariana, Tunis, Tunisia ' Institut Supérieur de Gestion de Tunis, Université de Tunis, LR99ES04 BESTMOD, Tunis, Tunisia

Abstract: Knowledge discovery from mobility data is about identifying behaviours from trajectories. In fact, mining masses of trajectories is required to have an overview of this data, notably, investigate the relationship between different entities movement. Most state-of-the-art work in this issue operates on raw trajectories. Nevertheless, behaviours discovered from raw trajectories are not as rich and meaningful as those discovered from semantic trajectories. In this paper, we establish a mining approach to extract patterns from semantic trajectories. We propose to apply sequential pattern mining based on a pre-processing step of clustering to alleviate the former's temporal complexity. Mining considers the spatial and temporal dimensions at different levels of granularity providing then richer and more insightful patterns about humans behaviour. We evaluate our work on tourists semantic trajectories in Kyoto. Results showed the effectiveness and efficiency of our model compared to state-of-the-art work.

Keywords: mobility data; trajectories; semantic modelling; sequential pattern mining; clustering; mobility pattern.

DOI: 10.1504/IJDMMM.2024.138825

International Journal of Data Mining, Modelling and Management, 2024 Vol.16 No.2, pp.148 - 175

Received: 04 Nov 2022
Accepted: 04 Oct 2023
Published online: 31 May 2024 *

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