Wensheng Gan

Muchuang Hu

Chien-Ming Chen

Proceedings of the IEEE Conference on Dependable and Secure Computing, 2021

A Reinforced Dynamic Multi-keyword Ranked Search with Forward Privacy.

[BibT_eX]

[DOI]

Proceedings of the IEEE Conference on Dependable and Secure Computing, 2021

Joint Utility and Frequency for Pattern Classification.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

NSPIS: Mining Negative Sequential Patterns with Individual Support.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

Mining On-shelf High-utility Quantitative Itemsets.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

TKQ: Top-K Quantitative High Utility Itemset Mining.

[BibT_eX]

[DOI]

Mourad Nouioua

Proceedings of the Advanced Data Mining and Applications - 17th International Conference, 2021

2020

Utility-Driven Mining of Trend Information for Intelligent System.

[BibT_eX]

[DOI]

Xuan Wang

ACM Trans. Manag. Inf. Syst., 2020

Utility Mining across Multi-Sequences with Individualized Thresholds.

[BibT_eX]

[DOI]

Trans. Data Sci., 2020

HUOPM: High-Utility Occupancy Pattern Mining.

[BibT_eX]

[DOI]

IEEE Trans. Cybern., 2020

Utility-Driven Data Analytics on Uncertain Data.

[BibT_eX]

[DOI]

Athanasios V. Vasilakos

IEEE Syst. J., 2020

ProUM: Projection-based utility mining on sequence data.

[BibT_eX]

[DOI]

Inf. Sci., 2020

TKUS: Mining Top-K High-Utility Sequential Patterns.

[BibT_eX]

[DOI]

CoRR, 2020

SESM: Emotional Social Semantic and Time Series Analysis of Learners' Comments.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics, 2020

UHUOPM: High Utility Occupancy Pattern Mining in Uncertain Data.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics, 2020

TopHUI: Top-k high-utility itemset mining with negative utility.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

OSUMI: On-Shelf Utility Mining from Itemset-based Data.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

2019

A Survey of Parallel Sequential Pattern Mining.

[BibT_eX]

[DOI]

ACM Trans. Knowl. Discov. Data, 2019

Correlated utility-based pattern mining.

[BibT_eX]

[DOI]

Inf. Sci., 2019

Fast Utility Mining on Complex Sequences.

[BibT_eX]

[DOI]

Jiexiong Zhang

CoRR, 2019

ProUM: High Utility Sequential Pattern Mining.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics, 2019

Utility-Driven Mining of High Utility Episodes.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2018

A survey of incremental high-utility itemset mining.

[BibT_eX]

[DOI]

Hamido Fujita

WIREs Data Mining Knowl. Discov., 2018

Extracting non-redundant correlated purchase behaviors by utility measure.

[BibT_eX]

[DOI]

Hamido Fujita

Knowl. Based Syst., 2018

Exploiting highly qualified pattern with frequency and weight occupancy.

[BibT_eX]

[DOI]

Ji Zhang

Knowl. Inf. Syst., 2018

PPSF: An Open-Source Privacy-Preserving and Security Mining Framework.

[BibT_eX]

[DOI]

Proceedings of the 2018 IEEE International Conference on Data Mining Workshops, 2018

Privacy Preserving Utility Mining: A Survey.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

CoUPM: Correlated Utility-based Pattern Mining.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

2017

Data mining in distributed environment: a survey.

[BibT_eX]

[DOI]

WIREs Data Mining Knowl. Discov., 2017

Cover Image, Volume 7, Issue 6.

[BibT_eX]

[DOI]

WIREs Data Mining Knowl. Discov., 2017

Efficiently mining uncertain high-utility itemsets.

[BibT_eX]

[DOI]

Soft Comput., 2017

FDHUP: Fast algorithm for mining discriminative high utility patterns.

[BibT_eX]

[DOI]

Knowl. Inf. Syst., 2017

Mining Weighted Frequent Itemsets without Candidate Generation in Uncertain Databases.

[BibT_eX]

[DOI]

Int. J. Inf. Technol. Decis. Mak., 2017

Mining of frequent patterns with multiple minimum supports.

[BibT_eX]

[DOI]

Eng. Appl. Artif. Intell., 2017

Extracting recent weighted-based patterns from uncertain temporal databases.

[BibT_eX]

[DOI]

Jimmy Ming-Tai Wu

Eng. Appl. Artif. Intell., 2017

Efficiently mining frequent itemsets with weight and recency constraints.

[BibT_eX]

[DOI]

Appl. Intell., 2017

Mining High-Utility Itemsets with Both Positive and Negative Unit Profits from Uncertain Databases.

[BibT_eX]

[DOI]

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2017

Extracting Non-redundant Correlated Purchase Behaviors by Utility Measure.

[BibT_eX]

[DOI]

Proceedings of the Big Data Analytics and Knowledge Discovery, 2017

Exploiting High Utility Occupancy Patterns.

[BibT_eX]

[DOI]

Proceedings of the Web and Big Data - First International Joint Conference, 2017

2016

Updating high-utility pattern trees with transaction modification.

[BibT_eX]

[DOI]

Multim. Tools Appl., 2016

Efficient mining of high-utility itemsets using multiple minimum utility thresholds.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2016

Efficient algorithms for mining high-utility itemsets in uncertain databases.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2016

FHN: An efficient algorithm for mining high-utility itemsets with negative unit profits.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2016

Efficiently Updating the Discovered Sequential Patterns for Sequence Modification.

[BibT_eX]

[DOI]

Int. J. Softw. Eng. Knowl. Eng., 2016

An efficient algorithm to maintain the discovered frequent sequences with record deletion.

[BibT_eX]

[DOI]

Intell. Data Anal., 2016

A fast maintenance algorithm of the discovered high-utility itemsets with transaction deletion.

[BibT_eX]

[DOI]

Intell. Data Anal., 2016

Maintaining the discovered high-utility itemsets with transaction modification.

[BibT_eX]

[DOI]

Appl. Intell., 2016

Weighted frequent itemset mining over uncertain databases.

[BibT_eX]

[DOI]

Appl. Intell., 2016

Fast algorithms for mining high-utility itemsets with various discount strategies.

[BibT_eX]

[DOI]

Adv. Eng. Informatics, 2016

Efficient Mining of Uncertain Data for High-Utility Itemsets.

[BibT_eX]

[DOI]

Proceedings of the Web-Age Information Management - 17th International Conference, 2016

More Efficient Algorithm for Mining Frequent Patterns with Multiple Minimum Supports.

[BibT_eX]

[DOI]

Proceedings of the Web-Age Information Management - 17th International Conference, 2016

Efficient algorithms for mining recent weighted frequent itemsets in temporal transactional databases.

[BibT_eX]

[DOI]

Proceedings of the 31st Annual ACM Symposium on Applied Computing, 2016

Efficient Mining of Weighted Frequent Itemsets in Uncertain Databases.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Data Mining in Pattern Recognition, 2016

More Efficient Algorithms for Mining High-Utility Itemsets with Multiple Minimum Utility Thresholds.

[BibT_eX]

[DOI]

Proceedings of the Database and Expert Systems Applications, 2016

Mining Recent High-Utility Patterns from Temporal Databases with Time-Sensitive Constraint.

[BibT_eX]

[DOI]

Proceedings of the Big Data Analytics and Knowledge Discovery, 2016

Mining Recent High Expected Weighted Itemsets from Uncertain Databases.

[BibT_eX]

[DOI]

Proceedings of the Web Technologies and Applications - 18th Asia-Pacific Web Conference, 2016

Mining Discriminative High Utility Patterns.

[BibT_eX]

[DOI]

Proceedings of the Intelligent Information and Database Systems - 8th Asian Conference, 2016

2015

Peer-to-peer usage analysis in dynamic databases.

[BibT_eX]

[DOI]

Peer-to-Peer Netw. Appl., 2015

Updating the Built Prelarge Fast Updated Sequential Pattern Trees with Sequence Modification.

[BibT_eX]

[DOI]

Int. J. Data Warehous. Min., 2015

Incrementally updating the discovered sequential patterns based on pre-large concept.

[BibT_eX]

[DOI]

Intell. Data Anal., 2015

RWFIM: Recent weighted-frequent itemsets mining.

[BibT_eX]

[DOI]

Eng. Appl. Artif. Intell., 2015

Efficient algorithms for mining up-to-date high-utility patterns.

[BibT_eX]

[DOI]

Adv. Eng. Informatics, 2015

A fast updated algorithm to maintain the discovered high-utility itemsets for transaction modification.

[BibT_eX]

[DOI]

Adv. Eng. Informatics, 2015

A Fast Algorithm to Maintain the Discovered High-Utility Itemsets with Modified Records.

[BibT_eX]

[DOI]

Proceedings of the 2015 IEEE International Conference on Systems, 2015

Hewin: High expected weighted itemset mining in uncertain databases.

[BibT_eX]

[DOI]

Proceedings of the 2015 International Conference on Machine Learning and Cybernetics, 2015

Mining high-utility itemsets with various discount strategies.

[BibT_eX]

[DOI]

Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, 2015

Mining High-Utility Itemsets with Multiple Minimum Utility Thresholds.

[BibT_eX]

[DOI]

Proceedings of the Eighth International C* Conference on Computer Science & Software Engineering, 2015

Mining Potential High-Utility Itemsets over Uncertain Databases.

[BibT_eX]

[DOI]

Proceedings of the ASE BigData & SocialInformatics 2015, 2015

Mining Weighted Frequent Itemsets with the Recency Constraint.

[BibT_eX]

[DOI]

Proceedings of the Web Technologies and Applications - 17th Asia-PacificWeb Conference, 2015

2014

Maintaining the discovered sequential patterns for sequence insertion in dynamic databases.

[BibT_eX]

[DOI]

Eng. Appl. Artif. Intell., 2014

Efficiently Maintaining the Fast Updated Sequential Pattern Trees With Sequence Deletion.

[BibT_eX]

[DOI]