Ham Nguyen

Orcid: 0000-0003-3253-4157

According to our database1, Ham Nguyen authored at least 15 papers between 2016 and 2026.

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Bibliography

2026
Efficiently mining frequent weighted utility closed patterns with pruning strategies from dynamic quantitative databases.
J. Supercomput., February, 2026

Fast and scalable sliding-window-based algorithms for mining frequent weighted utility patterns over dynamic quantitative data streams.
Inf. Sci., 2026

2024
An Efficient Algorithm for Mining Top-Rank-k Frequent Weighted Utility Itemsets in Hierarchical Quantitative Databases.
Proceedings of the International Conference on Computing and Communication Technologies, 2024

An Efficient Algorithm for Mining Closed Frequent Pattern on Quantitative Databases.
Proceedings of the International Conference on Computing and Communication Technologies, 2024

2023
Mining frequent weighted utility patterns with dynamic weighted items from quantitative databases.
Appl. Intell., August, 2023

A new approach for efficiently mining frequent weighted utility patterns.
Appl. Intell., 2023

2022
An Improving Approach for Top-Rank-k Frequent Pattern Mining.
Vietnam. J. Comput. Sci., 2022

Mining frequent weighted utility itemsets in hierarchical quantitative databases.
Knowl. Based Syst., 2022

An efficient approach for mining weighted uncertain interesting patterns.
Inf. Sci., 2022

Predicting Monthly Household Water Consumption.
Proceedings of the RIVF International Conference on Computing and Communication Technologies, 2022

Mining Frequent Weighted Utility Patterns in Dynamic Quantitative Databases.
Proceedings of the RIVF International Conference on Computing and Communication Technologies, 2022

2021
A Sliding Window-Based Approach for Mining Frequent Weighted Patterns Over Data Streams.
IEEE Access, 2021

2018
A weighted N-list-based method for mining frequent weighted itemsets.
Expert Syst. Appl., 2018

2016
An efficient algorithm for mining frequent weighted itemsets using interval word segments.
Appl. Intell., 2016

WUN-Miner: A new method for mining frequent Weighted Utility itemsets.
Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics, 2016


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