Adjoint Relation Mining Model of Key Personnel Based on Discrete Trajectory

doi:10.3969/j.issn.1671-1122.2021.12.005

Abstract

Abstract:

This paper proposes a method for mining key personnel adjoint relations based on discrete space trajectory matrix analysis. A mapping matrix between people and addresses is constructed for discrete space trajectories. The adjoint relations are identified through correlation analysis of the personnel address relationship matrix, and the discrete spatio-temporal trajectories are constructed. An adjoint relationship mining model based on effective distance judgments can mine the adjoint relationship of key personnel through features such as distance, time, and space. The experimental results show that the analysis method based on the discrete space trajectory matrix can quickly identify the people who have an adjoint relationship in the crowd, and given a certain key person, you can quickly find the people who have an adjoint relationship with them, and deal with these people. The number of adjoint persons is sorted, which is convenient for security personnel to trace and track in the future. In addition, the number of adjoint pairs is directly proportional to the effective distance to a certain extent, and the number of adjoint pairs is positively correlated with the increase of the amount of data.

Key words: discrete space trajectory, discrete spatio-temporal trajectory, key personnel, adjoint relation mining

CLC Number:

TP309

KANG Wenjie, ZHAO Wei, LIU Xuchong, SU Xin. Adjoint Relation Mining Model of Key Personnel Based on Discrete Trajectory[J]. Netinfo Security, 2021, 21(12): 31-37.

Figures/Tables 7

References 26

[1]	ZHENG Yu. Trajectory Data Mining: An Overview[J]. ACM Transactions on Intelligent Systems & Technology, 2015, 6(3):23-25.
[2]	SESTER M. Mining Group Movement Patterns[C]// ACM. ACM Sigspatial International Conference on Advances in Geographic Information Systems, November 5-7, 2013, Orlando, FL, United States. New York: ACM, 2013: 520-523.
[3]	LI Y, BAILEY J, KULIK L. Efficient Mining of Platoon Patterns in Trajectory Databases[M]. Amsterdam: Elsevier Science Publishers, 2015.
[4]	YUAN Guan, SUN Penghui, ZHAO Jie, et al. A Review of Moving Object Trajectory Clustering Algorithms[J]. Artificial Intelligence Review, 2017, 47(1):123-144. doi: 10.1007/s10462-016-9477-7 URL
[5]	WANG Shuang, WU Lina, ZHOU Fuchai, et al. Group Pattern Mining Algorithm of Moving Objects' Uncertain Trajectories[J]. International Journal of Computers Communications & Control, 2015, 10(3):428-440.
[6]	DENG Ze, HU Yangyang, ZHU Mao, et al. A Scalable and Fast OPTICS for Clustering Trajectory Big Data[J]. Cluster Computing, 2015, 18(2):549-562. doi: 10.1007/s10586-014-0413-9 URL
[7]	HU Wenbo, HUANG Wei, HU Guochao, et al. Trajectory Adjoint Pattern Analysis Based on OPTICS Clustering and Association Analysis[J]. Computer and Modernization, 2017, 16(12):82-87.
[8]	CAPDEVILA J, PERICACHO G, TORRES J, et al. Scaling DBSCAN Like Algorithms for Event Detection Systems in Twitter[M]. Heidelberg: Springer, 2016.
[9]	FAN Tianhui, GUO Naijing, REN Yujie. Consumer Clusters Detection with Geo-tagged Social Network Data Using DBSCAN Algorithm: A Case Study of the Pearl River Delta in China[J]. GeoJournal, 2021, 86(6):317-337. doi: 10.1007/s10708-019-10072-8 URL
[10]	PERAFN-LPEZ J C, SIERRA-PREZ J. An Unsupervised Pattern Recognition Methodology Based on Factor Analysis and a Genetic-DBSCAN Algorithm to Infer Operational Conditions from Strain Measurements in Structural Applications[J]. Chinese Journal of Aeronautics, 2021, 34(2):165-181. doi: 10.1016/j.cja.2020.09.035 URL
[11]	LI Yongnan. Research on Application of Spatial Patterns Mining in the Field of Counter Terrorism Intelligence Analysis[J]. Journal of Intelligence, 2018, 37(4):33-37.
	李勇男. 空间模式挖掘在反恐情报分析中的应用研究[J]. 情报杂志, 2018, 37(4):33-37.
[12]	LI Yongnan. Research on Application of Frequent Spatiotemporal Trajectory Pattern Mining in the Field of Counter Terrorism Intelligence Analysis[J]. Journal of Intelligence, 2018, 37(8):51-55.
	李勇男. 时空轨迹频繁模式在反恐情报分析中的应用研究[J]. 情报杂志, 2018, 37(8):51-55.
[13]	LI Wanqing, PENG Gao, FANG Fei, et al. Mining Method of Adjoint Behavior Pattern of Floating Vehicle Based on Big Data Analysis: China, CN105261218B[P]. 2017-08-08.
	李万清, 彭高, 方飞, 等. 基于大数据分析的浮动车伴随行为模式挖掘方法：中国,CN105261218B[P]. 2017-08-08.
[14]	CHEN Peng, QU Ke, CHEN Gang, et al. The Constitution of Individual Feature Dataset and Analysis of Suspect in Counter-terrorism[J]. Journal of Intelligence, 2018, 37(4):38-41, 68.
	陈鹏, 瞿珂, 陈刚, 等. 反恐背景下的个人特征数据构成与涉恐个体的挖掘分析[J]. 情报杂志, 2018, 37(4):38-41,68.
[15]	LI Yongnan. Clustering Analysis of Counter Terrorism Intelligence Based on Jaccard Index[J]. Modern Information, 2018, 38(1):51-55.
	李勇男. 基于雅卡尔系数的反恐情报聚类分析[J]. 现代情报, 2018, 38(1):51-55.
[16]	YANG Yang, JI Genlin, BAO Peiming. Algorithm for Mining Adjoint Pattern of Spatial-temporal Trajectory Based on Grid Index[J]. Computer Science, 2016, 43(1):107-110.
	杨阳, 吉根林, 鲍培明. 基于网格索引的时空轨迹伴随模式挖掘算法[J]. 计算机科学, 2016, 43(1):107-110.
[17]	HU Wenbo, HUANG Wei, HU Guochao. Trajectory Adjoint Pattern Analysis Based on OPTICS Clustering and Association Analysis[J]. Computer and Modernization, 2017(12):82-87.
	胡文博, 黄蔚, 胡国超. 基于OPTICS 聚类和关联分析的轨迹伴随模式分析[J]. 计算机与现代化, 2017(12):82-87.
[18]	XIA Huiyu. Navigational Risk Analysis Based on GIS Spatiotemporal Trajectory Mining: A Case Study in Nanjing Yangtze River Bridge Waters[J]. Arabian Journal of Geosciences, 2021, 14(3):1-15. doi: 10.1007/s12517-020-06304-8 URL
[19]	LIU Hongbin, WU Hao, SUN Weiwei, et al. Spatio-temporal GRU for Trajectory Classification[C]// IEEE. 2019 IEEE International Conference on Data Mining(ICDM), Noveber 8-11, 2019, Beijing, China. New Jersey: IEEE, 2020: 1228-1233.
[20]	JIN Fengmei, HUA Wen, ZHOU T, et al. Trajectory-based Spatiotemporal Entity Linking[EB/OL]. https://arxiv.org/abs/2010.01516v1, 2020-10-04.
[21]	ANSARI M Y, MAINUDDIN, AHMAD A, et al. Spatiotemporal Trajectory Clustering: A Clustering Algorithm for Spatiotemporal Data[EB/OL]. https://doi.org/10.1016/j.eswa.2021.115048, 2021-09-15.
[22]	SHANG Shuo, CHEN Lisi, ZHENG Kai, et al. Parallel Trajectory-to-Location Join[J]. IEEE Transactions on Knowledge & Data Engineering, 2019, 31(6):1194-1207.
[23]	PIELA M, KOTAS M, CONTRERAS-ORTIZ S H, et al. Spatio-temporal Filtering for Evoked Potentials Detection[C]// Springer. International Conference on Man-machine Interactions, October 2-4, 2019, Cracow, Poland. Heidelberg: Springer, 2019: 34-43.
[24]	REGENS J L, MOULD N, JENSEN III C J, et al. Effect of Intelligence Collection Training on Suspicious Activity Recognition by front Line Police Officers[J]. Security Journal, 2017, 30(3):951-962. doi: 10.1057/sj.2015.10 URL
[25]	LI Yongnan. Construction of Decision-making System of Counter Terrorism Intelligence Driven by Big Data[J]. Journal of Intelligence, 2018, 37(10):57-61.
	李勇男. 大数据驱动的反恐情报决策体系构建[J]. 情报杂志, 2018, 37(10):57-61.
[26]	LI Yongnan. Relational Analysis of Anti-terrorism Intelligence Based on Frequent Sequential Pattern Mining[J]. Information Studies: Theory & Application, 2018, 41(10):100-104, 46.
	李勇男. 基于频繁序列模式挖掘的反恐情报关联分析[J]. 情报理论与实践, 2018, 41(10):100-104,46.

脱敏的身份证ID	地址名	次数/次
LKIJJPIQQPIIJJILIK3B	***明明网吧	15
LKIJJPIQQPIIJKILIQ3B	***开心网吧	2
LKIJJPIQQPIJHKLHML3B	***大众网吧	1
LKIJJPIQQPIJHMILIX3B	***明明网吧	11
LKIJJPIQQPIJIHIJMK3B	***幻灵网吧	1
LKIJJPIQQPIJIIIJIN3B	***新浪网吧	2
LKIJJPIQQPIJIKILIX3B	***明明网吧	22
LKIJJPIQQPIJILHLIJ3B	***罗旧丁记网吧	1
LKIJJPIQQPIJJHHHJL3B	***飞宇网吧	1
LKIJJPIQQPIJJIMHMP3B	***飞宇网吧	1
LKIJJPIQQPIJJJINIP3B	***深蓝网吧	1
LKIJJPIQQPIJJKKLIN3B	***金连锁三星网吧分店	1
LKIJJPIQQPIJJNHHIQ3B	***梦工厂网咖旗舰店	1
LKIJJPIQQPIJKHILIM3B	***明明网吧	12
.......	........	...

序号	脱敏的身份证ID	次数/次
1	LKKHJOIQOKHIJJILIQ3B	40
2	LKIJJPIQQIIIHMILIO3B	40
3	LKKHJOIQKNHIJOILJP3B	38
4	LKKHJOIQLIHNILILIX3B	36
5	LKIJJPIQQLHQILLNJL3B	36
6	LKIJJPIQQJHKHIILIJ3B	35
7	LKIJJPIQPOHOIKILKX3B	34
8	LKIJJPIQOPIIIJILIK3B	32

阈值/km	1次	2次	4次	总人对数量/对
0.001	178944	2649	6	181599
0.01	179704	2649	6	182359
0.1	181327	2660	6	183993

阈值/km	1次	2次	3次	4次	5次	6次	总人对数量/对
0.001	1302657	44526	1818	192	3	58	1349254
0.01	1409123	44746	1827	192	3	58	1455949
0.1	1423718	45238	1851	192	3	58	1471060