基于改进密度聚类与模式信息挖掘的异常轨迹识别方法

doi:10.11959/j.issn.1000-436x.2017287

Journal on Communications ›› 2017, Vol. 38 ›› Issue (12): 21-33.doi: 10.11959/j.issn.1000-436x.2017287

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Abnormal trajectory detection method based on enhanced density clustering and abnormal information mining

Ming HE^1,²,Gong-da QIU¹,Bo ZHOU¹,Qiang LIU^1,³,Yu-ting CAO¹

¹ College of Command and Control Engineering，Army Engineering University of PLA，Nanjing 210007，China
² The 61st Research Institute of PLA，Beijing 100000，China
³ Naval Command College，Nanjing 210007，China

Revised:2017-11-12 Online:2017-12-01 Published:2018-01-19
Supported by:
The Natural Science Foundation of Jiangsu Province(BK20150721);The Natural Science Foundation of Jiangsu Province(BK20161469);China Postdoctoral Science Foundation(2015M582786);China Postdoctoral Science Foundation(2016T91017);The Primary Research ＆ Development Plan of Jiangsu Province(BE2015728);The Primary Research ＆ Development Plan of Jiangsu Province(BE2016904);The Engineering Research Center of Jiangsu Province(BM2014391);The National Key Research and Development Program of China(2016YFC0800606)

Abstract

Abstract:

Aiming at problems of low accuracy in the recognition and difficulty in enriching the information of abnormal behavior in the social security incidents，an abnormal trajectory detection method based on enhanced density clustering and abnormal information mining was proposed.Firstly，combined with Hausdorff distance，an enhanced DTW distance aiming at the problem of sampling to describe the behavior in detail was proposed.And based on the MBR distance， some definitions to describe the geographical distribution of trajectory were proposed.Secondly，with the density-distance decision model of CFSFDP algorithm，intelligent recognition of cluster was realized by using the difference of SSVR which was proposed based on SVR.Finally，based on the analysis of distribution under the two kinds of density，more abnormal information could be mined，three kinds of abnormal trajectories would be recognized.And the simulation results on trajectory data of Shanghai and Beijing verify that the algorithm is objective and efficient.Comparing to existing method，accuracy in the clustering is promoted by 10%，and the abnormal trajectories are sorted， abnormal information is enriched.

Key words: SVR, density clustering, abnormal trajectory detection, pattern information mining

CLC Number:

TP301

Ming HE,Gong-da QIU,Bo ZHOU,Qiang LIU,Yu-ting CAO. Abnormal trajectory detection method based on enhanced density clustering and abnormal information mining[J]. Journal on Communications, 2017, 38(12): 21-33.

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[1]	ZHENG Y . Trajectory data mining:an overview[J]. ACM Transactions on Intelligent Systems and Technology, 2015,6(3).
[2]	毛嘉莉, 金澈清, 章志刚 ,等. 轨迹大数据异常检测:研究进展及系统框架[J]. 软件学报, 2017,28(1): 17-34.
	MAO J L , JIN C Q , ZHANG Z G ,et al. Anomaly detection for trajectory big data:advancements and framework[J]. Journal of Software, 2017,28(1): 17-34.
[3]	ZHU J , JIANG W , LIU A ,et al. Time-dependent popular routes based trajectory outlier detection[C]// WISE. 2015: 16-30.
[4]	CHAWLA S , ZHENG Y , HU J . Inferring the root cause in road traffic anomalies[C]// IEEE International Conference on Data Mining. 2012: 141-150.
[5]	BIRANT D , KUT A . ST-DBSCAN:an algorithm for clustering spatial-temporal data[J]. Data＆Knowledge Engineering, 2007,60(1): 208-221.
[6]	TOOHEY K , DUCKHAM M . Trajectory similarity measures[J]. Sigspatial Special, 2015,7(1): 43-50.
[7]	魏龙翔, 何小海, 滕奇志 ,等. 结合Hausdorff距离和最长公共子序列的轨迹分类[J]. 电子与信息学报, 2013,35(4): 784-790.
	WEI L X , HE X H , TENG Q Z ,et al. Trajectory classification based on Hausdorff distance and longest common subsequence[J]. Journal of Electronics and Information Technology, 2013,35(4): 784-790.
[8]	YUAN G , SUN P , ZHAO J ,et al. A review of moving object trajectory clustering algorithms[J]. Artificial Intelligence Review, 2016: 1-22.
[9]	CHEN L , ?ZSU M , ORIA V . Robust and fast similarity search for moving object trajectories[C]// The 2005ACMSIGMOD International Conference on Management of Data. 2005: 491-502
[10]	CHEN L , NG R T . On the marriage of Lp-norms and edit distance[C]// VLDB. 2004: 792-803.
[11]	YUAN G , XIA S , ZHANG L ,et al. An efficient trajectory-clustering algorithm based on an index tree[J]. Transactions of the Institute of Measurement＆Control, 2012,34(7): 850-861.
[12]	NANNI M , PEDRESCHI D . Time-focused clustering of trajectories of moving objects[J]. Journal of Intelligent Information Systems, 2006,27(3): 267-289.
[13]	RODRIGUEZ A , LAIO A . Machine learning clustering by fast search and find of density peaks[J]. Science, 2014,344(6191): 1492-1496.
[14]	SHANG F , JIAO L C , SHI J ,et al. Fast density-weighted low-rank approximation spectral clustering[J]. Data Mining ＆ Knowledge Discovery, 2011,23(2): 345-378.
[15]	WANG J , WU J , WANG J ,et al. Multi-criteria decision-making methods based on the Hausdorff distance of hesitant fuzzy linguistic numbers[J]. Soft Computing, 2016,20(4): 1621-1633.
[16]	WEI L X , HE X H , TENG Q Z ,et al. Trajectory classification based on Hausdorff distance and longest common subsequence[J]. J Electron Inf Technol, 2013,35(4): 784-790
[17]	KHOSHAEIN V . Trajectory clustering using a variation of Fréchet distance[D]. Ottawa,Canada:University of Ottawa, 2014.
[18]	JEUNG H , YIUM L , JENSEN C S . Trajectory pattern mining[M]// Computing with Spatial Trajectories. Springer, 2011: 143-177.
[19]	郭虎升, 王文剑 . 动态粒度支持向量回归机[J]. 软件学报, 2013(11): 2535-2547.
	GUO H S , WANG W J . Dynamical granular support vector regression machine[J]. Journal of Software, 2013(11): 2535-2547.

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采样点数	ENDTW		DTW
采样点数	变异系数	均值	变异系数	均值
100%	—	4.07×10^?6	—	1.63×10^?4
80%	0.128	5.20×10^?6	0.111	1.98×10^?4
65%	0.177	5.66×10^?6	0.189	2.65×10^?4
50%	0.162	5.58×10^?6	0.214	3.29×10^?4
40%	0.212	6.68×10^?6	0.186	4.25×10^?4
	均值变异系数	0.173		0.380

Abnormal trajectory detection method based on enhanced density clustering and abnormal information mining

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