Faster Approximate Covering of Subcurves under the Fr\'echet Distance

TL;DR

This paper introduces a faster approximation algorithm for covering subcurves of polygonal curves under the Fréchet distance, improving efficiency for trajectory clustering tasks with theoretical guarantees.

Contribution

It presents a bicriteria-approximation algorithm using geometric set cover techniques, with near-linear expected runtime and applicability to high-dimensional curves.

Findings

Achieves O(k log k) line segments covering the curve within O(Δ) Fréchet distance.

Runs in expected time teredO(k^2 n + k n^3) for general curves.

Provides a variant with near-linear runtime using implicit weight updates.

Abstract

Subtrajectory clustering is an important variant of the trajectory clustering problem, where the start and endpoints of trajectory patterns within the collected trajectory data are not known in advance. We study this problem in the form of a set cover problem for a given polygonal curve: find the smallest number $k$ of representative curves such that any point on the input curve is contained in a subcurve that has Fr\'echet distance at most a given $\Delta$ to a representative curve. We focus on the case where the representative curves are line segments and approach this NP-hard problem with classical techniques from the area of geometric set cover: we use a variant of the multiplicative weights update method which was first suggested by Br\"onniman and Goodrich for set cover instances with small VC-dimension. We obtain a bicriteria-approximation algorithm that computes a set of $O(k\log(k))$ line segments that cover a given polygonal curve of $n$ vertices under Fr\'echet distance at most $O(\Delta)$. We show that the algorithm runs in $\widetilde{O}(k^2 n + k n^3)$ time in expectation and uses $ \widetilde{O}(k n + n^3)$ space. For two dimensional input curves that are $c$-packed, we bound the expected running time by $\widetilde{O}(k^2 c^2 n)$ and the space by $ \widetilde{O}(kn + c^2 n)$. In $\mathbb{R}^d$ the dependency on $n$ instead is quadratic. In addition, we present a variant of the algorithm that uses implicit weight updates on the candidate set and thereby achieves near-linear running time in $n$ without any assumptions on the input curve, while keeping the same approximation bounds. This comes at the expense of a small (polylogarithmic) dependency on the relative arclength.