Gengsen Huang

Orcid: 0000-0001-9780-8185

According to our database1, Gengsen Huang authored at least 17 papers between 2021 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

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Bibliography

2024
TaSPM: Targeted Sequential Pattern Mining.
ACM Trans. Knowl. Discov. Data, June, 2024

Towards utility-driven contiguous sequential patterns in uncertain multi-sequences.
Knowl. Based Syst., 2024

Privacy-preserving federated discovery of DNA motifs with differential privacy.
Expert Syst. Appl., 2024

2023
US-Rule: Discovering Utility-driven Sequential Rules.
ACM Trans. Knowl. Discov. Data, January, 2023

Data Analytic for Healthcare Cyber Physical System.
IEEE Trans. Netw. Sci. Eng., 2023

TALENT: Targeted Mining of Non-overlapping Sequential Patterns.
CoRR, 2023

Towards Top-K Non-Overlapping Sequential Patterns.
CoRR, 2023

Federated Learning for Metaverse: A Survey.
Proceedings of the Companion Proceedings of the ACM Web Conference 2023, 2023

Incremental Targeted Mining in Sequences.
Proceedings of the 10th IEEE International Conference on Data Science and Advanced Analytics, 2023

Towards Contiguous Sequences in Uncertain Data.
Proceedings of the 10th IEEE International Conference on Data Science and Advanced Analytics, 2023

USER: Towards High-Utility Sequential Rules with Repetitive Items.
Proceedings of the IEEE International Conference on Big Data, 2023

2022
Negative pattern discovery with individual support.
Knowl. Based Syst., 2022

Towards Sequence Utility Maximization under Utility Occupancy Measure.
CoRR, 2022

Towards Target Sequential Rules.
CoRR, 2022

Constraint-based Sequential Rule Mining.
Proceedings of the 9th IEEE International Conference on Data Science and Advanced Analytics, 2022

Flexibly Mining Better Patterns.
Proceedings of the IEEE International Conference on Big Data, 2022

2021
NSPIS: Mining Negative Sequential Patterns with Individual Support.
Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021


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