Dr. Wensheng Gan

Ph.D. Associate Professor

College of Cyber Security
Jinan University, Guangzhou, China

Office: Building D, Room 103, Panyu Campus
Email:  wsgan001 at gmail dot com

About me

Dr. Vincent W. Gan (甘文生) is currently an Associate Professor of computer science. He founded DSI (Data Science and Intelligence) Lab in 2020, and have been working as the director since then. DSI Lab is a research-oriented academic laboratory, providing the latest information on data mining research works and application tools to both academia and industry. He was a visiting scholar at Department of Computer Science, University of Illinois at Chicago, IL, USA, under the supervision by Prof. Philip S. Yu (ACM fellow & IEEE fellow) from September 2017 to March 2019. He received the M.S. and Ph.D. degrees in computer science from Harbin Institute of Technology (Shenzhen), Shenzhen, China, in January 2016 and December 2019, respectively, under the supervision by Prof. Han-Chieh Chao (IET fellow). He received the B.S. degree in Computer Science from South China Normal University, Guangzhou, China, in 2013. His research interests include data mining, utility mining, security and privacy-preserving, fuzzy theory, and big data technologies. He has published more than 100 research papers in peer-reviewed journals and international conferences.

Research

[Open Positions] We are recruiting Ph.D. students, Master students, and undergraduatestudents. Please feel free to send me an email with your CV. (wsgan001@gmail.com)

My principal research interest is Data Science and Engineering (DSE), e.g., big data mining in large-scale information and social data. I am more generally focus on data mining, big data analysis, statistics, machine learning, and network science, with a focus on modeling novel problems and proposing scalable algorithms for large-scale, real-world applications, including but not limited to: pattern mining, utility mining, complex sequence processing, graph data mining, utility computing with different learning models, and intelligent systems with big data.

In particular, I have extended conventional studies of Utility Mining: Theory, Techniques, and Applications. I have proposed a series of new models and algorithms to capture and predict the high-utility pattern and knoeledge on different types of data, and also studied how to explore both the different patterns of information diffusion and the utility property of the patterns (i.e., itemsets, rules, sequences, episodes and graphs) to better infer the hidden structure and knowledge of the rich data.

I am now looking for highly motivated Ph.D. and Master students: If you are interested in working with me, please feel free to email me. My recent research focuses on utility mining and computation with multi-source data, uncertain data, and complex events.

News and Highlights

  • 2019.10: The 2nd International Workshop on Utility-driven Mining (UDM 2019) in conjunction with ICDM 2019, Beijing, China.
  • 2019.9: The special issue of Utility-driven Mining in SCI journal IEEE Access (SCI, IF:4.02, JCR Q2). ([CFP])
  • 2018.7: The 1st International Workshop on Utility-driven Mining (UDM 2018) in conjunction with KDD 2018, London, UK.
  • 2018.4: A tutorial on Utility-driven Pattern Mining. Download slide decks for free!

Publications

[DBLP] [Google Scholar]

Survey papers

  1. “A Survey of Utility-Oriented Pattern Mining,” IEEE TKDE, 33(4): 1306-1327, 2020 [PDF].
  2. “A Survey of Parallel Sequential Pattern Mining,” ACM TKDD, 13(3): 25:1-25:34, 2019 [PDF].
  3. “Privacy Preserving Utility Mining: A Survey,” IEEE BigData, pp. 2617-2626, 2018 [PDF].
  4. “A Survey of Incremental High‐Utility Itemset Mining,” Wiley DMKD, 8(2), 2018 [PDF].
  5. “Data Mining in Distributed Environment: A Survey,” Wiley DMKD, 7(6), 2018 [PDF].

Journals (full list)

News!!! We will release all code & datasets of our papers, if you are interested in any of them, please fell free to contact me (wsgan001@gmail.com)

  1. Jiahui Chen, Shicheng Wan, Wensheng Gan, Guoting Chen, and Hamido Fujita, “TOPIC: Top-k High-Utility Itemset Discovering,” arxiv, 1-14 (2021) [PDF]
  2. Shicheng Wan, Jiahui Chen, Wensheng Gan, Guoting Chen, and Vikram Goyal, “THUE: Discovering Top-K High Utility Episodes,” arxiv, 1-14 (2021) [PDF]
  3. Chunkai Zhang, Zilin Du, Quanjian Dai, Wensheng Gan, Jian Weng, and Philip S. Yu, “TUSQ: Targeted High-Utility Sequence Querying,” IEEE Trans. Big Data., 1-15 (2021) [PDF]
  4. Wensheng Gan, Zilin Du, Weiping Ding, Chunkai Zhang, and Han-Chieh Chao, “Explainable Fuzzy Utility Mining on Sequences,” IEEE Trans. Fuzzy Syst., 1-15 (2021) [PDF]
  5. Chien-Ming Chen, Lili Chen, Wensheng Gan, Lina Qiu, Weiping Ding, “Discovering high utility-occupancy patterns from uncertain data,” Information Science., 546: 1208-1229 (2021) [PDF]
  6. Chunkai Zhang, Zilin Du, Wensheng Gan, and Philip S. Yu, “TKUS: Mining top-k high utility sequential patterns,” Information Science., 570: 342-359 (2021) [PDF]
  7. Chunkai Zhang, Zilin Du, Yuting Yang, Wensheng Gan, and Philip S. Yu, “On-shelf Utility Mining of Sequence Data,” ACM Trans. Knowl. Discov. Data., 16(2): 21:1-21:31 (2021) [PDF]
  8. Wensheng Gan, Jerry Chun-Wei Lin, Jiexiong Zhang, Hongzhi Yin, Philippe Fournier-Viger, Han-Chieh Chao, and Philip S. Yu, “Utility Mining Across Multi-Dimensional Sequences,” ACM Trans. Knowl. Discov. Data., 15(5): 82:1-82:24 (2021) [PDF]
  9. Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, Han-Chieh Chao, and Philip S. Yu, “Beyond Frequency: Utility Mining with Varied Item-specific Minimum Utility,” ACM Trans. Internet Techn., 21(1): 3:1-3:32 (2021) [PDF]
  10. Wensheng Gan, Jerry Chun-Wei Lin, Jiexiong Zhang, and Philip S. Yu, “Utility Mining across Multi-Sequences with Individualized Thresholds,” ACM Trans. Data Sci., 1(2): 8:1-8:29 (2020) [PDF]
  11. Wensheng Gan, Jerry Chun-Wei Lin, Han-Chieh Chao, Philippe Fournier-Viger, Xuan Wang, and Philip S. Yu, “Utility-Driven Mining of Trend Information for Intelligent System,” ACM Trans. Manag. Inf. Syst., 11(3): 14:1-14:28 (2020) [PDF]
  12. Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, Han-Chieh Chao, Vincent S. Tseng, and Philip S. Yu, “A Survey of Utility-Oriented Pattern Mining,” IEEE Trans. Knowl. Data Eng., 33(4): 1306-1327 (2021). [PDF]
  13. Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, Han-Chieh Chao, and Philip S. Yu, “A Survey of Parallel Sequential Pattern Mining,” ACM Trans. Knowl. Discov. Data, 13(3): 25:1-25:34, 2019. [PDF]
  14. Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, Han-Chieh Chao, and Philip S. Yu, “HUOPM: High Utility Occupancy Pattern Mining,” IEEE Trans. Cybernetics, 2019. [PDF] [Code & Datasets]
  15. Wensheng Gan, Jerry Chun-Wei Lin, Jiexiong Zhang, Philippe Fournier-Viger, Han-Chieh Chao, and Philip S. Yu, “Fast Utility Mining on Sequence Data,” IEEE Trans. Cybernetics, 2020. [PDF] [Code & Datasets]
  16. Wensheng Gan, Jerry Chun-Wei Lin, Jiexiong Zhang, and Philip S. Yu, “Utility Mining Across Multi-Sequences with Individualized Thresholds,” ACM Trans. Data Science, 2019. [PDF] [Code & Datasets]
  17. Wensheng Gan, Jerry Chun-Wei Lin, Han-Chieh Chao, Athanasios V Vasilakos, and Philip S. Yu, “Utility-driven Data Analytics on Uncertain Data,” IEEE System Journal, 2019. [PDF] [Code & Datasets]
  18. Wensheng Gan, Jerry Chun-Wei Lin, Jiexiong Zhang, Han-Chieh Chao, Hamido Fujita, and Philip S. Yu, “ProUM: Projection-based Utility Mining on Sequence Data,” Information Science, online, 2019. [PDF] [Code & Datasets]
  19. Wensheng Gan, Jerry Chun-Wei Lin, Han-Chieh Chao, Hamido Fujita, and Philip S. Yu, “Utility-based Correlated Pattern Mining,” Information Science, vol. 504, pp. 470-486, 2019. [PDF] [Code & Datasets]
  20. Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, Han-Chieh Chao, Tzung-Pei Hong, and Hamido Fujita, “A Survey of Incremental High-Utility Itemset Mining,” Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery, vol. 8(2), 2018 [PDF]
  21. Wensheng Gan, Jerry Chun-Wei Lin, Han-Chieh Chao, and Justin Zhan, “Data Mining in Distributed Environment: A Survey,” Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery, vol. 7(6), 2017 [PDF]
  22. Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, Han-Chieh Chao, Justin Zhan, and Ji Zhang, “Exploiting Highly Qualified Pattern with Frequency and Weight Occupancy,” Knowledge and Information Systems, vol. 56(1), pp. 165-196, 2018 [PDF] [Code & Datasets]
  23. Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, Han-Chieh Chao, Justin Zhan, and Ji Zhang, “Extracting Non-Redundant Correlated Purchase Behaviors by Utility Measure,” Knowledge-Based Systems, vol. 143, pp. 30 - 41, 2018 [PDF] [Code & Datasets]
  24. Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, Han-Chieh Chao, Jimmy Ming-Thai Wu, and Justin Zhan, “Extracting Recent Weighted-based Patterns from Uncertain Temporal Databases,” Engineering Applications of Artificial Intelligence, vol. 61, pp. 161-172, 2017 [PDF] [Code & Datasets]
  25. Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, Han-Chieh Chao, and Justin Zhan, “Mining of Frequent Patterns with Multiple Minimum Supports,” Engineering Applications of Artificial Intelligence, vol. 60, pp. 83-96, 2017 [PDF] [Code & Datasets]
  26. Jerry Chun-Wei Lin, Wensheng Gan, Tzung-Pei Hong, and Vincent S. Tseng, “Efficient algorithms for mining up-to-date high-utility patterns,” Advanced Engineering Informatics, vol. 29(3), pp. 648-661, 2016 [PDF] [Code & Datasets]
  27. Jerry Chun-Wei Lin, Wensheng Gan, Philippe Fournier-Viger, Tzung-Pei Hong, and Vincent S. Tseng, “Efficient algorithms for mining high-utility itemsets in uncertain databases,” Knowledge-Based Systems, vol. 96, pp. 171-187, 2016 [PDF] [Code & Datasets]
  28. Jerry Chun-Wei Lin, Wensheng Gan, Philippe Fournier-Viger, Tzung-Pei Hong, and Vincent S. Tseng, “Fast algorithms for mining high-utility itemsets with various discount strategies,” Advanced Engineering Informatics, vol. 30(2), pp. 109-126, 2016 [PDF] [Code & Datasets]

Conferences

  1. Wensheng Gan, Jerry Chun-Wei Lin, Han-Chieh Chao, Hamido Fujita, and Philip S. Yu, “ProUM: High Utility Sequential Pattern Mining,” IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 777-783, 2019. (EI) [PDF]
  2. Wensheng Gan, Jerry Chun-Wei Lin, Han-Chieh Chao, Shyue-Liang Wang, and Philip S. Yu, “Privacy Preserving Utility Mining: A Survey,” IEEE International Conference on Big Data (Big Data), pp. 2617-2626, 2018 (EI) [PDF]
  3. Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, and Han-Chieh Chao, “Exploiting High Utility Occupancy Patterns,” APWeb-WAIM, pp. 239-247, 2017. (EI) [PDF]
  4. Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, and Han-Chieh Chao, “Extracting Non-Redundant Correlated High Utility Purchase Behaviors by Utility Measure,” The 18th International Conference Big Data Analytics and Knowledge Discovery (DaWak), pp. 433-446, 2017. (EI) [PDF]
  5. Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, Han-Chieh Chao, and Vincent S. Tseng, “Mining High-Utility Itemsets with Both Positive and Negative Unit Profits from Uncertain Databases,” The 21th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 434-446, 2017. (EI) [PDF]
  6. Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, and Han-Chieh Chao, “More Efficient Algorithms for Mining High-Utility Itemsets with Multiple Minimum Utility Thresholds,” The 27th International Conference Database and Expert Systems Applications (DEXA), pp. 71-87, 2016. (EI) [PDF]
  7. Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, and Han-Chieh Chao, “Mining Recent High-Utility Patterns from Temporal Databases with Time-Sensitive Constraint,” The 17th International Conference Big Data Analytics and Knowledge Discovery (DaWak), pp. 3-18, 2016. (EI) [PDF]
  8. Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, and Han-Chieh Chao, “Mining Recent High Expected Weighted Itemset from Uncertain Databases,” The 18th Asia Pacific Web Conference (APWeb), pp. 581-593, 2016. (EI) [PDF]
  9. Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, and Han-Chieh Chao, “More Efficient Algorithm for Mining Frequent Patterns with Multiple Minimum Supports,” The 17th International Conference on Web-Age Information Management (WAIM), pp. 3-16, 2016. (EI) [PDF]

Professional Services

Publish Editor

TPC Member & Reviewer

  • Utility-Driven Mining Workshop (UDM 2018) with SIGKDD, 2018
  • Utility-Driven Mining Workshop (UDM 2019) with IEEE ICDM, 2019
  • International Conference ICGEC 2017, 2018, 2019
  • International Conference Data Analytics 2017, 2018, 2019
  • IADIS Intern. Conference on Information Systems 2018, 2019
  • International Conference GraphSM 2018, 2019

Journal Reviewer

  • IEEE Transactions on Knowledge and Data Engineering (TKDE, SCI:4.3)
  • ACM Trans. on Knowledge Discovery from Data (TKDD, SCI:2.538)
  • IEEE Transactions on Cybernetics (TCYB, SCI, IF:10.387, JCR Q1)
  • IEEE Transactions on Neural Networks and Learning Systems (TNNLS, SCI, IF:12.05, JCR Q1)
  • IEEE Transactions on Industrial Electronics (TIE, SCI, IF:6.30, JCR Q1)
  • IEEE Biomedical and Health Informatics (JBHI, SCI, IF:3.850, JCR Q1)
  • Knowledge Based Systems (KBS, SCI, IF:5.10, JCR Q1, CCF C)
  • Pattern Recognition (SCI, IF:4.582, JCR Q1, CCF B)
  • Future Generation Computer Systems (FGCS, SCI, IF:5.768, JCR Q1, CCF C)
  • Applied Intelligence (APIN, SCI, IF:2.882, JCR Q2, CCF C)
  • IEEE Access (SCI, IF:4.000, JCR Q1)
  • World Wide Web Journal (SCI, IF:1.771, JCR Q3, CCF B)
  • Frontiers of Computer Science (SCI, IF:1.129, JCR Q3)

Other information

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