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

Turn off MathJax

Article Contents

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

Next Previous

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

Citation:

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

Citation:

PDF( 3897 KB)

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

More Information

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

FullText(HTML)

References(31)

References

[1]	KROODSMA D A, MAYORGA J, HOCHBERG T, et al. Tracking the global footprint of fisheries[J]. Science, 2018, 359: 904-908. doi: 10.1126/science.aao5646
[2]	XIAO Fang-liang, LIGTERINGEN H, VAN GULIJK C, et al. Comparison study on AIS data of ship traffic behavior[J]. Ocean Engineering, 2015, 95: 84-93. doi: 10.1016/j.oceaneng.2014.11.020
[3]	ZHANG Wei-bin, GOERLANDT F, KUJALA P, et al. An advanced method for detecting possible near miss ship collisions from AIS data[J]. Ocean Engineering, 2016, 124: 141-156. doi: 10.1016/j.oceaneng.2016.07.059
[4]	CHEN Zhi-jun, XUE Jie, WU Chao-zhong, et al. Classification of vessel motion pattern in inland waterways based on automatic identification system[J]. Ocean Engineering, 2018, 161: 69-76. doi: 10.1016/j.oceaneng.2018.04.072
[5]	TU En-mei, ZHANG Guang-hao, RACHMAWATI L, et al. Exploiting AIS data for intelligent maritime navigation: a comprehensive survey from data to methodology[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(5): 1559-1582. doi: 10.1109/TITS.2017.2724551
[6]	KIM J H, CHOI J H, YOO K H, et al. AA-DBSCAN: an approximate adaptive DBSCAN for finding clusters with varying densities[J]. The Journal of Supercomputing, 2019, 75(1): 142-169. doi: 10.1007/s11227-018-2380-z
[7]	DING Zhao-ying, YAO Di, WU Lin, et al. A dock mining algorithm for massive vessel location data based on improved DBSCAN[J]. Computer Engineering and Science, 2015, 37(11): 2061-2067. (in Chinese). doi: 10.3969/j.issn.1007-130X.2015.11.011
[8]	LIU Tao, HU Qin-you, YANG Chun. Clustering analysis and identification of traffic congested waters[J]. Navigation of China, 2010, 33(4): 75-78. (in Chinese). doi: 10.3969/j.issn.1000-4653.2010.04.018
[9]	PALLOTTA G, VESPE M, BRYAN K. Vessel pattern knowledge discovery from AIS data: a frame work for anomaly detection and route prediction[J]. Entropy, 2013, 15: 2218-2245. doi: 10.3390/e15062218
[10]	ZHAO Liang-bin, SHI Guo-you, YANG Jia-xuan. Adaptive hierarchical clustering of ship trajectory with DBSCAN algorithm[J]. Navigation of China, 2018, 41(3): 53-58. (in Chinese). doi: 10.3969/j.issn.1000-4653.2018.03.011
[11]	WU Lin, XU Yong-jun, WANG Qi, et al. Mapping global shipping density from AIS data[J]. The Journal of Navigation, 2017, 70: 67-81. doi: 10.1017/S0373463316000345
[12]	Marine Management Organisation. Mapping UK shipping density and routes technical annex[R]. Welsh: Marine Management Organisation, 2014.
[13]	XIAO Xiao, ZHAO Qiang, SHAO Zhe-ping, et al. Specific ship's encounter live distribution based on AIS[J]. Navigation of China, 2014, 37(3): 50-53. (in Chinese). doi: 10.3969/j.issn.1000-4653.2014.03.012
[14]	NING Jian-qiang, HUANG Tao, DIAO Bo-yu, et al. A fine grained grid-based maritime traffic density algorithm for mass ship trajectory data[J]. Computer Engineering and Science, 2015, 37(12): 2242-2249. (in Chinese). doi: 10.3969/j.issn.1007-130X.2015.12.008
[15]	LIU Chun-fang, HUANG Wen-bin, SUN Fu-chun, et al. LDS-FCM: a linear dynamical system based fuzzy C-means method for tactile recognition[J]. IEEE Transactions on Fuzzy Systems, 2019, 27(1): 72-83. doi: 10.1109/TFUZZ.2018.2859184
[16]	XU Chao, ZHAN Tian-ming, KONG Ling-cheng, et al. A robust hierarchical fuzzy algorithm with student's t-distribution for image segmentation application[J]. Acta Electronica Sinica, 2017, 45(7): 1695-1700. (in Chinese). doi: 10.3969/j.issn.0372-2112.2017.07.020
[17]	DING Yi, FU Xian. Kernel-based fuzzy C-means clustering algorithm based on genetic algorithm[J]. Neurocomputing, 2016, 188: 233-238. doi: 10.1016/j.neucom.2015.01.106
[18]	LU Wei-jia, YAN Zhuang-zhi. Improved FCM algorithm based on K-means and granular computing[J]. Journal of Intelligent Systems, 2015, 24(2): 215-222. doi: 10.1515/jisys-2014-0119
[19]	LI Qiang, ZHANG Qi-jun, GUAN Xin, et al. Segmentation of carotid intima media in ultrasound images using improved fuzzy C-means algorithm[J]. Journal of Tianjin University (Science and Technology), 2018, 51(1): 95-102. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-TJDX201801014.htm
[20]	WU Zi-heng, WU Zhong-cheng, ZHANG Jun. An improved FCM algorithm with adaptive weights based on SA-PSO[J]. Neural Computing and Applications, 2017, 28(10): 3113-3118. doi: 10.1007/s00521-016-2786-6
[21]	YU De-xin, TIAN Xiu-juan, YANG Zhao-sheng. Division of traffic control periods based on improved FCM clustering[J]. Journal of South China University of Technology (Natural Science Edition), 2016, 44(12): 53-60. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HNLG201612008.htm
[22]	XI Liang, WANG Yong, ZHANG Feng-bin. Anomaly detection algorithm based on FCM with adaptive artificial fish-swarm[J]. Journal of Computer Research and Development, 2019, 56(5): 1048-1059. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JFYZ201905015.htm
[23]	ZHOU Kai-le, YANG Shan-lin, WANG Xiao-jia, et al. Load classification based on improved FCM algorithm with adaptive fuzziness parameter selection[J]. System Engineering—Theory and Practice, 2014, 34(5): 1283-1289. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XTLL201405024.htm
[24]	GONG Gai-yun, GAO Xin-bo, WU Zhong-dong. An optimal choice method of parameter m in FCM clustering algorithm[J]. Fuzzy Systems and Mathematics, 2005, 19(1): 143-148. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-MUTE200501024.htm
[25]	WANG Jun, WANG Shi-tong. Double indices FCM algorithm based on hybrid distance metric learning[J]. Journal of Software, 2010, 21(8): 1878-1888. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-RJXB201008012.htm
[26]	WANG Zong-hu, LIU Zhi-jing, CHEN Dong-hui. Research of PSO-based fuzzy C-means clustering algorithm[J]. Computer Science, 2012, 39(9): 166-169. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA201209039.htm
[27]	ZHOU Shi-bo, XU Wei-xiang, CHAI Tian. Data-weighted fuzzy C-means clustering algorithm[J]. Systems Engineering and Electronic, 2014, 36(11): 2314-2319. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XTYD201411033.htm
[28]	RODRIGUEZ A, LAIO A. Clustering by fast search and find of density peaks[J]. Science, 2014, 344: 1492-1496.
[29]	PAL N R, BEZDEK J C. On cluster validity for the fuzzy C-mean model[J]. IEEE Transactions on Fuzzy Systems, 1995, 3(3): 370-379.
[30]	XIE Juan-ying, GAO Hong-chao, XIE Wei-xin, et al. Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors[J]. Information Sciences, 2016, 354: 19-40.
[31]	JIANG Ke-qin, SHI Pei-bei. Optimized initial centers for fuzzy C-means algorithm[J]. Journal of Hefei University of Technology, 2009, 32(5): 762-764, 768. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HEFE200905037.htm

Relative Articles

Supplements(0)

Cited By

Get Citation

PDF

XML

Article Metrics

Article views (992) PDF downloads(358)

Aggregation characteristics of anchored vessels based on optimized FCM algorithm

doi: 10.19818/j.cnki.1671-1637.2019.06.013

Abstract

References

Catalog

Article Metrics

Related

Aggregation characteristics of anchored vessels based on optimized FCM algorithm

doi: 10.19818/j.cnki.1671-1637.2019.06.013

Abstract

References

Catalog

Article Metrics

Related

Export File

Citation

Format

Content