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An algorithm for high-dimensional traffic data clustering

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

Record type: Book Section


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.

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


Local EPrints ID: 53292
ISBN: 9783540459163
ISSN: 0302-9743
PURE UUID: 26acd0df-4a44-4526-b845-0304ce39cd15

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Date deposited: 22 Jul 2008
Last modified: 17 Jul 2017 14:38

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Author: Pengjun Zheng
Author: Mike McDonald

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