The University of Southampton
University of Southampton Institutional Repository

An algorithm for high-dimensional traffic data clustering

An algorithm for high-dimensional traffic data clustering
An algorithm for high-dimensional traffic data clustering
High-dimensional fuzzy clustering may converge to a local optimum that is significantly inferior to the global optimal partition. In this paper, a two-stage fuzzy clustering method is proposed. In the first stage, clustering is applied on the compact data that is obtained by dimensionality reduction from the full-dimensional data. The optimal partition identified from the compact data is then used as the initial partition in the second stage clustering based on full-dimensional data, thus effectively reduces the possibility of local optimum. It is found that the proposed two-stage clustering method can generally avoid local optimum without computation overhead. The proposed method has been applied to identify optimal day groups for traffic profiling using operational traffic data. The identified day groups are found to be intuitively reasonable and meaningful.
9783540459163
0302-9743
59-68
Springer
Zheng, Pengjun
a46dbafc-a753-4f22-b825-a00fd36ebd44
McDonald, Mike
943ab1e7-1f5a-4e85-bf7e-f90495729b88
Zheng, Pengjun
a46dbafc-a753-4f22-b825-a00fd36ebd44
McDonald, Mike
943ab1e7-1f5a-4e85-bf7e-f90495729b88

Zheng, Pengjun and McDonald, Mike (2006) An algorithm for high-dimensional traffic data clustering. In, Fuzzy Systems and Knowledge Discovery. (Lecture Notes in Computer Science, 4223/2006) Berlin / Heidelberg. Springer, pp. 59-68. (doi:10.1007/11881599_8).

Record type: Book Section

Abstract

High-dimensional fuzzy clustering may converge to a local optimum that is significantly inferior to the global optimal partition. In this paper, a two-stage fuzzy clustering method is proposed. In the first stage, clustering is applied on the compact data that is obtained by dimensionality reduction from the full-dimensional data. The optimal partition identified from the compact data is then used as the initial partition in the second stage clustering based on full-dimensional data, thus effectively reduces the possibility of local optimum. It is found that the proposed two-stage clustering method can generally avoid local optimum without computation overhead. The proposed method has been applied to identify optimal day groups for traffic profiling using operational traffic data. The identified day groups are found to be intuitively reasonable and meaningful.

This record has no associated files available for download.

More information

Published date: 28 September 2006
Additional Information: Book Chapter: Fuzzy Systems and Knowledge Discovery, ISBN: 978-3-540-45916-3

Identifiers

Local EPrints ID: 53292
URI: http://eprints.soton.ac.uk/id/eprint/53292
ISBN: 9783540459163
ISSN: 0302-9743
PURE UUID: 26acd0df-4a44-4526-b845-0304ce39cd15

Catalogue record

Date deposited: 22 Jul 2008
Last modified: 15 Mar 2024 10:40

Export record

Altmetrics

Contributors

Author: Pengjun Zheng
Author: Mike McDonald

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×