Aggregation characteristics of anchored vessels based on optimized FCM algorithm

ZHOU Shi-bo; TANG Ji-hong; XIONG Zhen-nan

doi:10.19818/j.cnki.1671-1637.2019.06.013

Volume 19 Issue 6

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Article Navigation > Journal of Traffic and Transportation Engineering > 2019 > 19(6): 137-148

ZHOU Shi-bo, TANG Ji-hong, XIONG Zhen-nan. Aggregation characteristics of anchored vessels based on optimized FCM algorithm[J]. Journal of Traffic and Transportation Engineering, 2019, 19(6): 137-148. doi: 10.19818/j.cnki.1671-1637.2019.06.013

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ZHOU Shi-bo, TANG Ji-hong, XIONG Zhen-nan. Aggregation characteristics of anchored vessels based on optimized FCM algorithm[J]. Journal of Traffic and Transportation Engineering, 2019, 19(6): 137-148. doi: 10.19818/j.cnki.1671-1637.2019.06.013

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Aggregation characteristics of anchored vessels based on optimized FCM algorithm

doi: 10.19818/j.cnki.1671-1637.2019.06.013

School of Navigation, Jimei University, Xiamen 361021, Fujian, China

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Author Bio:
ZHOU Shi-bo(1978-), male, associate professor, PhD, jmusailor@163.com

XIONG Zhen-nan (1965-), male, professor, 1965znxiong@163.com
Received Date: 2019-06-11
Publish Date: 2019-12-25

Abstract

Abstract

For the lack of sensitivity of clustering results to noise sample points due to the random selection of initial clustering centers by fuzzy C-means(FCM) algorithm, by using the method of local density weighting, the selection range of the initial clustering centers was limited to the region of sample points with high local density, and the selection method of the initial clustering centers was optimized. The local density of sample points was used to improve the objective function, and then improve the influence of sample points with higher local density in the iterative process of the objective function, so that the clustering performance of FCM algorithm was promoted. The clustering effect of improved local density FCM(LD-FCM) algorithm was verified by artificial dataset and iris real dataset. The anchoring preference was analyzed by calculating the local density of anchored vessel's position data. Experimental result shows that compared with the FCM algorithm, the clustering accuracy rate, recall rate, and F-measure of the optimized LD-FCM algorithm improve by 2.9%, 3.8%, and 3.9%, respectively, which shows that the performance of the optimized LD-FCM algorithm is better than that of the FCM algorithm. The clustering results on the anchored vessels location data correctly reflect the aggregation characteristics and anchoring preference in Tianjin Port, and are consistent with the general practice of the vessels, which shows that the optimized LD-FCM algorithm is an effective way to analyze the aggregation characteristics and anchoring preference.
- waterway transportation,
- anchored vessel,
- fuzzy C-means algorithm,
- location data,
- data mining,
- aggregation characteristic,
- anchoring preference

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References(31)

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Aggregation characteristics of anchored vessels based on optimized FCM algorithm

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