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  • Chen J, Zhou J and Hao X. (2024). A Time-Efficient Distributed Constant Conditional Functional Dependency Discovery Algorithm for Data Consistency 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC). 10.1109/COMPSAC61105.2024.00036. 979-8-3503-7696-8. (198-203).

    https://ieeexplore.ieee.org/document/10633433/

  • Liu D, Li Y, Baskett W, Lin D and Shyu C. (2022). RHPTree—Risk Hierarchical Pattern Tree for Scalable Long Pattern Mining. ACM Transactions on Knowledge Discovery from Data. 16:4. (1-33). Online publication date: 31-Aug-2022.

    https://doi.org/10.1145/3488380

  • Weng C and Huang C. (2020). Discovering Specific Sales Patterns Among Different Market Segments. International Journal of Data Warehousing and Mining. 16:3. (37-59). Online publication date: 1-Jul-2020.

    https://doi.org/10.4018/IJDWM.2020070103

  • Garzón-Garnica E, Cano-Olivos P, Sánchez-Partida D and Martínez-Flores J. (2020). Data Mining/Mediation to Evaluate Risk of a Humanitarian Logistics Network in Mexico. Techniques, Tools and Methodologies Applied to Global Supply Chain Ecosystems. 10.1007/978-3-030-26488-8_16. (359-381).

    http://link.springer.com/10.1007/978-3-030-26488-8_16

  • Elhilbawi H, Eldawlatly S and Mahdi H. (2019). A Taxonomy of Discretization Techniques based on Class Labels and Attributes' Relationship 2019 14th International Conference on Computer Engineering and Systems (ICCES). 10.1109/ICCES48960.2019.9068185. 978-1-7281-5260-8. (316-321).

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  • Song C, He Y, Bo Y, Wang J, Ren Z, Guo J and Yang H. (2019). Disease relative risk downscaling model to localize spatial epidemiologic indicators for mapping hand, foot, and mouth disease over China. Stochastic Environmental Research and Risk Assessment. 10.1007/s00477-019-01728-5. 33:10. (1815-1833). Online publication date: 1-Oct-2019.

    http://link.springer.com/10.1007/s00477-019-01728-5

  • Sheri A, Rafique M, Hassan M, Junejo K and Jeon M. Boosting Discrimination Information Based Document Clustering Using Consensus and Classification. IEEE Access. 10.1109/ACCESS.2019.2923462. 7. (78954-78962).

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  • Abuzaid F, Bailis P, Ding J, Gan E, Madden S, Narayanan D, Rong K and Suri S. (2018). MacroBase. ACM Transactions on Database Systems. 43:4. (1-45). Online publication date: 31-Dec-2019.

    https://doi.org/10.1145/3276463

  • Spadini D, Palomba F, Zaidman A, Bruntink M and Bacchelli A. (2018). On the Relation of Test Smells to Software Code Quality 2018 IEEE International Conference on Software Maintenance and Evolution (ICSME). 10.1109/ICSME.2018.00010. 978-1-5386-7870-1. (1-12).

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  • Zhou J, Cheng Q and Li S. iCFDMiner. Proceedings of the 2018 International Conference on Computing and Data Engineering. (15-21).

    https://doi.org/10.1145/3219788.3219808

  • García-Borroto M. (2018). A Restriction-Based Approach to Generalizations. Progress in Artificial Intelligence and Pattern Recognition. 10.1007/978-3-030-01132-1_27. (239-246).

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  • Chavary E, Erfani S and Leckie C. Summarizing Significant Changes in Network Traffic Using Contrast Pattern Mining. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. (2015-2018).

    https://doi.org/10.1145/3132847.3133111

  • Shan J, Zhang H, Liu W and Liu Q. Research of Classification Impact Factors Mining Based on the Contrast Pattern Equivalence Classes. Proceedings of the 2017 International Conference on Wireless Communications, Networking and Applications. (138-143).

    https://doi.org/10.1145/3180496.3180621

  • Bailis P, Gan E, Madden S, Narayanan D, Rong K and Suri S. MacroBase. Proceedings of the 2017 ACM International Conference on Management of Data. (541-556).

    https://doi.org/10.1145/3035918.3035928

  • ZHOU J, DIAO X, CAO J and PAN Z. (2016). An Optimization Strategy for CFDMiner: An Algorithm of Discovering Constant Conditional Functional Dependencies. IEICE Transactions on Information and Systems. 10.1587/transinf.2015EDL8170. E99.D:2. (537-540).

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  • Hassan M, Karim A, Kim J and Jeon M. (2015). CDIM. Information Sciences: an International Journal. 316:C. (87-106). Online publication date: 20-Sep-2015.

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    https://doi.org/10.1109/TKDE.2015.2411609

  • Liu X, Wu J, Gu F, Wang J and He Z. (2014). Discriminative pattern mining and its applications in bioinformatics. Briefings in Bioinformatics. 10.1093/bib/bbu042. 16:5. (884-900). Online publication date: 1-Sep-2015.

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  • Tseng V, Cheng-Wei Wu , Fournier-Viger P and Yu P. (2015). Efficient Algorithms for Mining the Concise and Lossless Representation of High Utility Itemsets. IEEE Transactions on Knowledge and Data Engineering. 27:3. (726-739). Online publication date: 1-Mar-2015.

    https://doi.org/10.1109/TKDE.2014.2345377

  • Nguyen H and Cao J. (2015). Preference-Based Top-k Representative Skyline Queries on Uncertain Databases. Advances in Knowledge Discovery and Data Mining. 10.1007/978-3-319-18032-8_22. (280-292).

    https://link.springer.com/10.1007/978-3-319-18032-8_22

  • Li J, Liu J, Toivonen H, Satou K, Sun Y and Sun B. (2014). Discovering statistically non-redundant subgroups. Knowledge-Based Systems. 67. (315-327). Online publication date: 1-Sep-2014.

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  • Li M, Wang H and Li Y. Sectional and Conditional Functional Dependencies. Proceedings of the 9th International Conference on Wireless Algorithms, Systems, and Applications - Volume 8491. (793-803).

    https://doi.org/10.1007/978-3-319-07782-6_71

  • Junejo K and Karim A. (2013). Robust personalizable spam filtering via local and global discrimination modeling. Knowledge and Information Systems. 34:2. (299-334). Online publication date: 1-Feb-2013.

    https://doi.org/10.1007/s10115-012-0477-x

  • Hamrouni T. (2012). Key roles of closed sets and minimal generators in concise representations of frequent patterns. Intelligent Data Analysis. 16:4. (581-631). Online publication date: 1-Jul-2012.

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  • Hassan M and Karim A. Clustering and understanding documents via discrimination information maximization. Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I. (566-577).

    https://doi.org/10.1007/978-3-642-30217-6_47

  • Fan W, Geerts F, Li J and Xiong M. (2011). Discovering Conditional Functional Dependencies. IEEE Transactions on Knowledge and Data Engineering. 23:5. (683-698). Online publication date: 1-May-2011.

    https://doi.org/10.1109/TKDE.2010.154

  • Villerd J, Toussaint Y and Louët A. Adverse drug reaction mining in pharmacovigilance data using formal concept analysis. Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III. (386-401).

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  • Villerd J, Toussaint Y and Louët A. Adverse drug reaction mining in pharmacovigilance data using formal concept analysis. Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III. (386-401).

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    https://doi.org/10.1007/978-3-642-15939-8_25

  • Feng M, Dong G, Li J, Tan Y and Wong L. (2010). PATTERN SPACE MAINTENANCE FOR DATA UPDATES AND INTERACTIVE MINING*. Computational Intelligence. 10.1111/j.1467-8640.2010.00360.x. 26:3. (282-317). Online publication date: 1-Aug-2010.

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  • Ngo T, Feng M, Liu G and Wong L. Efficiently finding the best parameter for the emerging pattern-based classifier PCL. Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I. (121-133).

    https://doi.org/10.1007/978-3-642-13657-3_15

  • Feng M, Li J, Wong L and Tan Y. Negative Generator Border for Effective Pattern Maintenance. Proceedings of the 4th international conference on Advanced Data Mining and Applications. (217-228).

    https://doi.org/10.1007/978-3-540-88192-6_21

  • Li J and Yang Q. (2007). Strong Compound-Risk Factors. IEEE Transactions on Information Technology in Biomedicine. 11:5. (544-552). Online publication date: 1-Sep-2007.

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  • Li J, Liu G and Wong L. Mining statistically important equivalence classes and delta-discriminative emerging patterns. Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. (430-439).

    https://doi.org/10.1145/1281192.1281240

  • Feng M, Dong G, Li J, Tan Y and Wong L. Evolution and maintenance of frequent pattern space when transactions are removed. Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining. (489-497).

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  • Hamrouni T, Valtchev P, Yahia S and Nguifo E. About the lossless reduction of the minimal generator family of a context. Proceedings of the 5th international conference on Formal concept analysis. (130-150).

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  • Feng M, Dong G, Li J, Tan Y and Wong L. Evolution and Maintenance of Frequent Pattern Space When Transactions Are Removed. Advances in Knowledge Discovery and Data Mining. 10.1007/978-3-540-71701-0_50. (489-497).

    http://link.springer.com/10.1007/978-3-540-71701-0_50

  • Hamrouni T, Valtchev P, Yahia S and Nguifo E. About the Lossless Reduction of the Minimal Generator Family of a Context. Formal Concept Analysis. 10.1007/978-3-540-70901-5_9. (130-150).

    http://link.springer.com/10.1007/978-3-540-70901-5_9

  • Hamrouni T, Yahia S and Nguifo E. Succinct system of minimal generators. Proceedings of the 4th international conference on Concept lattices and their applications. (80-95).

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  • Hamrouni T, Ben Yahia S and Mephu Nguifo E. Succinct System of Minimal Generators: A Thorough Study, Limitations and New Definitions. Concept Lattices and Their Applications. (80-95).

    https://doi.org/10.1007/978-3-540-78921-5_5

  • Loekito E and Bailey J. Fast mining of high dimensional expressive contrast patterns using zero-suppressed binary decision diagrams. Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. (307-316).

    https://doi.org/10.1145/1150402.1150438