董祥军

齐鲁工业大学计算机科学与技术学院教授，博士后，博士生导师，山东省人工智能学会副理事长，山东省计算机学会理事，山东物联网协会常务理事，山东物联网协会教育专委会副会长，ACM（中国）会员，中国指挥与控制学会认知与行为专委会常务会员，国家自然基金委通讯评议专家，北京、浙江、山东等多个省市的科技计划通讯评议专家。曾任齐鲁工业大学信息学院副院长、院长。2001年评为副教授，2005年破格评为教授，2009.8-2010.2悉尼科技大学（UTS）访问学者。主持国家自然科学基金面上项目2项，中国博士后基金、山东省自然科学基金、山东省研究生教育创新计划项目等纵向课题10余项，主持横向课题近10项，参与国家级、省级纵向课题10余项。长期从事数据挖掘方面的研究，在Artificial Intelligence、IEEE TNNLS、IEEE TCYB、Pattern Recognition、CIKM国际知名期刊和会议上发表论文100多篇，其中SCI收录30多篇，EI收录40多篇，授权发明专利5项。获高校优秀科研成果三等奖2项。讲授数据结构、数据库原理、计算机专业英语、电子商务等本科生课程和数据挖掘与知识发现研究生课程，曾获得校首届“青年教学优秀奖”、校“优秀共产党员”和校“优秀研究生指导教师”等荣誉称号。指导全日制硕士研究生30人，毕业21人，其中7人考取了澳大利亚悉尼科技大学（UTS）、北京理工大学、武汉大学、华中科技大学、中国海洋大学、北京邮电大学的博士生，2人获得山东省优秀硕士论文，3人获得校级优秀硕士论文，3人获得国家奖学金。任国际著名期刊“IEEE TKDE”、“IEEE Intelligent Systems”、“Knowledge based Systems”等的审稿人，国际知名会议PAKDD、PRICAI、ADMA、AusAI程序委员会委员。主要研究方向：数据挖掘技术、人工智能等。

主要项目：

1. 国家自然基金面上项目，宽松约束的负序列模式及规则挖掘关键技术研究(主持，62076143, 2021.01-2024.12)

2. 国家自然基金面上项目，负序列模式挖掘关键技术及其在医保欺诈检测中的应用研究(主持， 71271125,2013.1-2016.12)；

3. 山东省自然基金面上项目，基于项缺失的负序列模式快速挖掘技术及筛选机制研究（主持，ZR2018MF011，2018.3-2020.12）

4. 山东省研究生导师能力提升计划项目，硕士研究生的学术创新能力培养机制研究（主持，2017.1-2019.12）

其它项目：

5. 教育部产学协同育人项目，物联网数据挖掘（主持，2017.7-2018.6）

6. 齐鲁工业大学国际合作项目，负序列模式关键技术研究及其在心电图监测中的应用（主持，2019.1-2021.12）

7. 山东省自然科学基金项目，负序列模式关键技术的研究（主持，项目编号：ZR2011FM028,2011.7-2014.7）；

8. 山东省自然科学基金项目，多数据库间的负关联规则挖掘技术研究（主持，Y2007G25）；

9. 山东省中青年科学家奖励基金项目，负关联规则关键技术的研究（主持， 2006BS01017）；

10. 中国博士后科学基金项目面上项目，非频繁项集挖掘及冗余规则修剪技术（主持，20070420302）；

11. 山东省研究生教育创新计划项目，IT研究生RAI培养模式的研究（主持，SDYY09037）；

12. 山东省教育厅科技计划项目，水库大坝信息集成与智能决策系统（主持， J06N06）；

中国发明专利：

1.基于逻辑推理负关联规则修剪技术的客户购买行为分析方法,2020

2.一种基于选取合适聚类数目的聚类算法的数字图像处理方法，2019

3.重复负序列模式在客户购买行为分析中的应用,2018

4.正负序列模式筛选方法在客户购买行为分析中的应用,2018

5.    多支持度的正负序列模式在客户购买行为分析中的应用,2018

代表性著作：

1.   Xiangjun Dong, Yongshun Gong, Longbing Cao: e-RNSP: An Efficient Method for Mining Repetition Negative Sequential Patterns.  IEEE Transactions on Cybernetics, 50(5): 2084-2096 (2020) (中科院分区(下同) ,SCI-Q1, IF=11.079)

2.   Xinming Gao, Yongshun Gong, Tiantian Xu, Jinhu Lu, Yuanhai Zhao, Xiangjun Dong*. Towards to Better Structure and Looser Constraint to Mine Negative Sequential Patterns. IEEE Transactions on Neural Networks and Learning Systems. (DOI: 10.1109/TNNLS.2020.3041732 , SCI-Q1, IF=8.793）

3.   Ping Qiu, Yongshun Gong, Yuhai Zhao, Longbing Cao, Chengqi Zhang, and Xiangjun Dong*. An Efficient Method for Modeling Non-occurring Behaviors by Negative Sequential Patterns with Loose Constraints. IEEE Transactions on Neural Networks and Learning Systems.(10.1109/TNNLS.2021.3063162, SCI-Q1, IF=8.793)

4.     Yongshun Gong, Zhibin Li, Jian Zhang*, Wei Liu, Bei Chen, Xiangjun Dong*. A Spatial Missing Value Imputation Method for Multi-view Urban Statistical Data. IJCAI 2020: 1310-1316(CCF-A，录用率12.6%)

5.   Dong Xiangjun, Qiu ping, Lv Jinhu, Cao Longbing, Xu Tiantian. Mining Top-k Useful Negative Sequential Patterns via Learning. IEEE Transactions on Neural Networks and Learning Systems, vol: 30, no. 9, 2019,  2764-2778  (SCI-Q1, IF= 11.683）

6.   XiangjunDong; Yongshun Gong; Longbing Cao. F-NSP+:A fast negative sequential patterns mining method with self-adaptive data storage. Pattern Recognition, 2018, 84: 13~27 ; (SCI-Q1, IF= 7.196)

7.     Yuhai Zhao*, Xiangjun Dong*, Ying Yin: Effective and Efficient Dense Subgraph Query in Large-Scale Social Internet of Things. IEEE Transastion on Industrial Informatics, 16(4): 2726-2736 (2020) （SCI-Q1, IF=9.112）

其它论文列表：

8.   Longbing Cao, Xiangjun Dong, Zhigang Zheng. e-NSP: Efficient Negative Sequential Pattern Mining. Artificial Intelligence, 2016, 235: 156-182（CCF-A）

9.     Ying Yin, Yuhai Zhao, He Li, Xiangjun Dong. Multi-objective evolutionary clustering for large-scale dynamic community detection. Information Science, 269-287 (2021) (SCI-Q1, IF=5.91)

10.           Xiangjun Dong*, Feng Hao, Long Zhao, Tiantian Xu. An efficient method for pruning redundant negative and positive association rules. Neurocomputing 393: 245-258 (2020)

11.  Jinguang Sun，Tao Li，Hua Yan，Xiangjun Dong*. Research on an expression classification method based on a probability graph model. Multimedia Tools and Applications, 79(45), 2020, pp. 34029-34043

12.           Yue Lu, Long Zhao*, Zhao Li and Xiangjun Dong*.Genetic Similarity Analysis Based on Positive and Negative Sequence Patterns of DNA. Symmetry 2020, 12, 2090; (doi:10.3390/sym12122090)(SCI-Q3)

13.           Mengjiao Zhang, Tiantian Xu*, Zhao Li, Xiqing Han, Xiangjun Dong*. e-HUNSR: An Efficient Algorithm for Mining High Utility Negative Sequential Rules. Symmetry 12(8): 1211 (2020)

14.           Xiangjun Dong, Zhigang Zheng, Longbing Cao, et al. e-NSP: Efficient Negative Sequential Pattern Mining Based on Identified Positive Patterns Without Database Rescanning.Proceedings of 19th Association of Computing Machinery Conference on Information and Knowledge Management (CIKM2011), Glasgow, 825-830

15.           Can Wang, Xiangjun Dong, Fei Zhou, et al. Coupled Attribute   Similarity Learning on Categorical Data, in IEEE Transactions on Neural  Networks and Learning Systems, vol. 26, no. 4, pp. 781-797, April 2015. (SCI-Q1, IF=11.683)

16.           Xiaoqi Jiang, Tiantian Xu, Xiangjun Dong*. Campus Data Analysis Based on Positive and Negative Sequential Patterns. International Journal of Pattern Recognition and Artificial Intelligence,33(5): 1959016:1-1959016:16. 2019 (SCI-Q4, IF=0.915).

17.  Guangyao Dong, Han Yan, Guohua Lv*, Xiangjun Dong*: Exploring the Utilization of Gradient Information in SIFT Based Local Image Descriptors. Symmetry 11(8): 998 (2019) (SCI-Q3, IF=2.645)

18.           Tiantian Xu; Tongxuan Li; Xiangjun Dong*. Efficient High Utility Negative Sequential Patterns Mining in Smart Campus. IEEE ACCESS, 2018, 6: 23839~23847 ; (SCI-Q2, IF= 3.557)

19.           Qiu Ping, Jiang, Xiaoqi Hao Feng), Xu Tiantian*, Dong Xiangjun*. Mining Negative Sequential Patterns from Frequent and Infrequent Sequences Based on Multiple Level Minimum Supports. Filomat. Vol. 32, no.5, 2018,1765-1776 (SCI-Q4, IF= 0.635)

20.           Tiantian Xu; Jianliang Xu*; Xiangjun Dong*. Mining High Utility Sequential Patterns using Multiple Minimum Utility. International Journal of Pattern Recognition and Artificial Intelligence, 2018.3.18 10, 32(10) ; (SCI-Q4, IF=1.029)

21.  Chunpeng Wang, Simiao Wang, Bin Ma, Jian Li, Xiangjun Dong, Zhiqiu Xia: Transform Domain Based Medical Image Super-resolution via Deep Multi-scale Network. ICASSP 2019: 2387-2391

22.           Qiu Ping, Zhao Long, Chen Weiyang, Xu Tiantian*, Dong Xiangjun*. Mining negative sequential patterns from infrequent positive sequences with 2-level multiple minimum supports. Filomat. Vol. 32, no.5, 2018,1875-1885 (SCI-Q4, IF= 0.635)

23.           Chen, Weiyang, Li, Weiwei, Dong, Xiangjun, Pei, Jialun. A Review of Biological Image Analysis. CURRENT BIOINFORMATICS. Vol.13, no.4,2018,  337-343 (SCI-Q4, IF=0.627)

24.           Jialun Pei, Long Zhao, Xiangjun Dong*, et al. Effective algorithm for determining the number of clusters and its application in image segmentation. Cluster Computing 20(4): 2845-2854 (2017) (SCI-Q3, IF=2.04)

25.           Long Zhao, Qian Gao, Xiangjun Dong*, et al. K- local maximum margin feature extraction algorithm for churn prediction in telecom. Cluster Computing, 2017, 20(2):1401-1409. (SCI-Q3, IF=2.040)

26.           Jialun Pei, Weiyang Chen*, Xiangjun Dong*, Magnetic Resonance Imaging   Brain Image Segmentation Method Based on Adaptive Clustering Algorithm, Journal of Medical Imaging and Health Informatics, 2017,7:1-7(SCI-Q4, IF=0.621)

27.            Long Zhao, Linfeng Jiang*, Xiangjun Dong*. Supervised feature   selection method via potential value estimation. Cluster Computing,   2016:1-11. (SCI-Q3, IF=1.514)

28.           Long Zhao，Xue Dong，WeiYang Chen，LinFeng Jiang，Xiangjun Dong*. The combined   cloud model for edge detection. Multimedia Tools and Applications, 2017,   76(13):15007-15026. (SCI-Q3, IF=1.530)

29.            Yongshun Gong, Tiantian Xu, Xiangjun Dong*, et al. e-NSPFI: Efficient Mining Negative Sequential Pattern from both Frequent and Infrequent Positive Sequential Patterns. International Journal of Pattern Recognition and Artificial Intelligence, Vol.31, Issue 02, 2017. (SCI-Q4, IF=0.915)

30.            Tiantian Xu, Xiangjun Dong*, Jianliang Xu, Yongshun Gong. E-msNSP:   Efficient negative sequential patterns mining based on multiple minimum   supports. International Journal of Pattern Recognition and Artificial   Intelligence, Volume 31, Issue 02, 2017. (SCI-Q4, IF=0.915)

31.            Tiantian Xu, Xiangjun Dong*, Jianliang Xu et al.. Mining High Utility Sequential Patterns with Negative Item Values. International Journal of Pattern Recognition and Artificial Intelligence, 2017.5.4 10, 31(10): 1750035 ; (SCI-Q4, IF=0.915) 

32.           Chen, Weiyang, Liao, Bo, Li, Weiwei, Dong, Xiangjun*, et al. Segmenting Microscopy Images of Multi-Well Plates Based on Image Contrast. Microscopy and Microanalysis. Vol.23, no. 5, 2017,932-937 (SCI-Q3,IF=2.124)

33.            Zhao Long, Hao Feng, Xu Tiantian, Dong Xiangjun. Positive and Negative Association Rules Mining for Mental Health Analysis of College Students. EURASIA Journal of Mathematics Science and Technology Education, 2017, 13(8):5577-5587.(SSCI-Q3)

34.           Liang Hu, Zhao Shengrong, Dong Xiangjun. The brain MRI image sparse representation based on the gradient information and the non-symmetry and anti-packing model. Computer Assisted Surgery, 2017, 22(sup1):106-112. (SCI-Q4,IF=0.489)

35.            Xiangjun Dong，Chuanlu Liu，Tiantian Xu. Select Actionable Positive or Negative Sequential Patterns. Journal of Intelligent and Fuzzy Systems，2015.1.1，29（6）：2759-2767.(SCI-Q4, IF=1.004)

山东省人工智能学会副理事长，山东省计算机学会理事，山东物联网协会常务理事，山东物联网协会教育专委会副会长，ACM（中国）会员，中国指挥与控制学会认知与行为专委会常务会员，国家自然基金委通讯评议专家，北京、浙江、山东等多个省市的科技计划通讯评议专家