Mining frequency-based sequential trajectory co-clusters

TL;DR

This paper introduces a novel trajectory co-clustering method that automatically identifies meaningful mobility patterns by considering element frequency and sequence order, overcoming previous limitations of threshold dependence.

Contribution

The proposed method jointly clusters trajectories and their elements using frequency and an objective function, eliminating the need for user-defined thresholds and sequence-ignoring assumptions.

Findings

Finds frequent, meaningful contiguous sequences in real-world data.

Reveals relevant mobility patterns effectively.

Outperforms existing methods in pattern discovery.

Abstract

Co-clustering is a specific type of clustering that addresses the problem of finding groups of objects without necessarily considering all attributes. This technique has shown to have more consistent results in high-dimensional sparse data than traditional clustering. In trajectory co-clustering, the methods found in the literature have two main limitations: first, the space and time dimensions have to be constrained by user-defined thresholds; second, elements (trajectory points) are clustered ignoring the trajectory sequence, assuming that the points are independent among them. To address the limitations above, we propose a new trajectory co-clustering method for mining semantic trajectory co-clusters. It simultaneously clusters the trajectories and their elements taking into account the order in which they appear. This new method uses the element frequency to identify candidate co-clusters. Besides, it uses an objective cost function that automatically drives the co-clustering process, avoiding the need for constraining dimensions. We evaluate the proposed approach using real-world a publicly available dataset. The experimental results show that our proposal finds frequent and meaningful contiguous sequences revealing mobility patterns, thereby the most relevant elements.