(k, l)-Medians Clustering of Trajectories Using Continuous Dynamic Time Warping

12/01/2020
by   Milutin Brankovic, et al.
0

Due to the massively increasing amount of available geospatial data and the need to present it in an understandable way, clustering this data is more important than ever. As clusters might contain a large number of objects, having a representative for each cluster significantly facilitates understanding a clustering. Clustering methods relying on such representatives are called center-based. In this work we consider the problem of center-based clustering of trajectories. In this setting, the representative of a cluster is again a trajectory. To obtain a compact representation of the clusters and to avoid overfitting, we restrict the complexity of the representative trajectories by a parameter l. This restriction, however, makes discrete distance measures like dynamic time warping (DTW) less suited. There is recent work on center-based clustering of trajectories with a continuous distance measure, namely, the Fréchet distance. While the Fréchet distance allows for restriction of the center complexity, it can also be sensitive to outliers, whereas averaging-type distance measures, like DTW, are less so. To obtain a trajectory clustering algorithm that allows restricting center complexity and is more robust to outliers, we propose the usage of a continuous version of DTW as distance measure, which we call continuous dynamic time warping (CDTW). Our contribution is twofold: 1. To combat the lack of practical algorithms for CDTW, we develop an approximation algorithm that computes it. 2. We develop the first clustering algorithm under this distance measure and show a practical way to compute a center from a set of trajectories and subsequently iteratively improve it. To obtain insights into the results of clustering under CDTW on practical data, we conduct extensive experiments.

READ FULL TEXT

page 1

page 4

research
03/09/2022

Computing Continuous Dynamic Time Warping of Time Series in Polynomial Time

Dynamic Time Warping is arguably the most popular similarity measure for...
research
10/30/2018

Feature Trajectory Dynamic Time Warping for Clustering of Speech Segments

Dynamic time warping (DTW) can be used to compute the similarity between...
research
02/18/2021

No-Substitution k-means Clustering with Low Center Complexity and Memory

Clustering is a fundamental task in machine learning. Given a dataset X ...
research
09/25/2017

On improved bound for measure of cluster structure in compact metric spaces

A compact metric space (X, ρ) is given. Let μ be a Borel measure on X. B...
research
12/02/2021

Trajectory Clustering Performance Evaluation: If we know the answer, it's not clustering

Advancements in Intelligent Traffic Systems (ITS) have made huge amounts...
research
07/11/2019

Void-and-Cluster Sampling of Large Scattered Data and Trajectories

We propose a data reduction technique for scattered data based on statis...
research
01/11/2023

Fast conformational clustering of extensive molecular dynamics simulation data

We present an unsupervised data processing workflow that is specifically...

Please sign up or login with your details

Forgot password? Click here to reset