A clustering method for misaligned curves

TL;DR

This paper introduces a clustering method for misaligned curves that aligns and groups similar shapes, demonstrating effectiveness through simulations and real data applications.

Contribution

It proposes a novel clustering approach that accounts for misalignment by combining shape similarity with warping function constraints.

Findings

Effective clustering demonstrated in simulations

Method performs well with different parameters

Successful application to real data sets

Abstract

We consider the problem of clustering misaligned curves. According to our similarity measure, two curves are considered similar if they have the same shape after being aligned, and the warping function does not differ from the identity function very much. A clustering method is proposed, which updates curves so that similar curves become more similar, and then combines curves that are similar enough to form clusters. The proposed method needs to be used together with a clustering index and a set of combination thresholds. Simulation results are presented to demonstrate the performance of this approach under different parameter settings and clustering indexes. Two real data applications are included.