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.
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Zheng, P., McDonald, M. (2006). An Algorithm for High-Dimensional Traffic Data Clustering. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_8
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DOI: https://doi.org/10.1007/11881599_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45916-3
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