The group proposes and evaluates algorithms for artificial intelligence and machine learning, data mining and big data analysis. Two specific topics that have been and are being worked on are on advanced algorithms in sequential pattern and rule mining and on the development of algorithms for sequential pattern and rule mining from sequence databases.
Topic
- Machine learning
- Data Mining
- Big data analytics
Member
- Prof. Dr. Le Hoai Bac
- Dr. Nguyen Ngoc Thao
- Dr. Bui Tien Len
- MCs. Duong Van Hai (PhD student)
- MCs. Van Thi Thien Trang (PhD student)
- MCs. Tran Trung Kien
- MCs. Le Ngoc Thanh
Research
- Advanced Algorithms in Sequential and Rule Pattern Mining (Nafosted, 2016-2018)
- Develop algorithms for mining sequential patterns and rules from sequence databases. (Nafosted, 2014-2016)
Possible collaborative activities
- Deploying projects on Datamining
- Data analysis
- Exploiting user opinions
- Data hiding
Published
- Bac Le, Ut Huynh, Duy-Tai Dinh.
A pure array structure and parallel strategy for high-utility sequential pattern mining. Expert Syst. Appl. 104: 107-120 (2018)
- Bac Le, Phuc Luong.
Optimized cardinality-based generalized itemset mining using transaction ID and numeric encoding. Appl. Intell. 48(8): 2067-2080 (2018)
- Bac Le, Duy-Tai Dinh, Van-Nam Huynh, Quang-Minh Nguyen, Philippe Fournier-Viger.
An efficient algorithm for Hiding High Utility Sequential Patterns. Int. J. Approx. Reasoning 95: 77-92 (2018)
- Bac Le, Hai V. Duong, Tin C. Truong, Philippe Fournier-Viger.
FCloSM, FGenSM: two efficient algorithms for mining frequent closed and generator sequences using the local pruning strategy. Knowl. Inf. Syst. 53(1): 71-107 (2017)
- Hoai Bac Le, Minh-Thai Tran, Bay Vo.
Mining frequent closed inter-sequence patterns efficiently using dynamic bit vectors. Appl. Intell. 43(1): 74-84 (2015)
