Patrick G. Clark
According to our database1,
Patrick G. Clark
authored at least 45 papers
between 2011 and 2023.
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Bibliography
2023
Global and saturated probabilistic approximations based on generalized maximal consistent blocks.
Log. J. IGPL, March, 2023
Complexity of Rule Sets Mined from Incomplete Data Using Probabilistic Approximations Based on Characteristic Sets.
Proceedings of the Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 27th International Conference KES-2023, 2023
2021
Complexity of rule sets in mining incomplete data using characteristic sets and generalized maximal consistent blocks.
Log. J. IGPL, 2021
Mining Incomplete Data Using Global and Saturated Probabilistic Approximations Based on Characteristic Sets and Maximal Consistent Blocks.
Proceedings of the Rough Sets - International Joint Conference, 2021
2020
Complexity of Rule Sets Mined from Incomplete Data Using Probabilistic Approximations Based on Generalized Maximal Consistent Blocks.
Proceedings of the Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 24th International Conference KES-2020, 2020
Mining Data with Many Missing Attribute Values Using Global and Saturated Probabilistic Approximations Based on Characteristic Sets.
Proceedings of the Information and Software Technologies - 26th International Conference, 2020
2019
Mining Incomplete Data - A Comparison of Concept and New Global Probabilistic Approximations.
Proceedings of the Intelligent Decision Technologies 2019, 2019
Complexity of Rule Sets Induced from Data with Many Lost Values and "Do Not Care" Conditions.
Proceedings of the Intelligent Systems Design and Applications, 2019
Rule Set Complexity in Mining Incomplete Data Using Global and Saturated Probabilistic Approximations.
Proceedings of the Information and Software Technologies - 25th International Conference, 2019
2018
Characteristic sets and generalized maximal consistent blocks in mining incomplete data.
Inf. Sci., 2018
A Comparison of Characteristic Sets and Generalized Maximal Consistent Blocks in Mining Incomplete Data.
Proceedings of the Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations, 2018
A Comparison of Concept and Global Probabilistic Approximations Based on Mining Incomplete Data.
Proceedings of the Information and Software Technologies - 24th International Conference, 2018
Complexity of Rule Sets Induced by Characteristic Sets and Generalized Maximal Consistent Blocks.
Proceedings of the Artificial Intelligence and Soft Computing, 2018
2017
Inf. Technol. Control., 2017
Proceedings of the Intelligent Decision Technologies 2017 - Proceedings of the 9th KES International Conference on Intelligent Decision Technologies (KES-IDT 2017), 2017
A Comparison of Four Classification Systems Using Rule Sets Induced from Incomplete Data Sets by Local Probabilistic Approximations.
Proceedings of the Foundations of Intelligent Systems - 23rd International Symposium, 2017
Complexity of Rule Sets Induced by Two Versions of the MLEM2 Rule Induction Algorithm.
Proceedings of the Artificial Intelligence and Soft Computing, 2017
2016
A comparison of two MLEM2 rule induction algorithms extended to probabilistic approximations.
J. Intell. Inf. Syst., 2016
Rule Set Complexity for Incomplete Data Sets with Many Attribute-Concept Values and "Do Not Care" Conditions.
Proceedings of the Rough Sets - International Joint Conference, 2016
Complexity of Rule Sets Induced from Data Sets with Many Lost and Attribute-Concept Values.
Proceedings of the Artificial Intelligence and Soft Computing, 2016
2015
Int. J. Netw. Secur., 2015
Proceedings of the 2015 International Conference on Soft Computing and Software Engineering, 2015
Proceedings of the Rough Sets and Knowledge Technology - 10th International Conference, 2015
On the Number of Rules and Conditions in Mining Data with Attribute-Concept Values and "Do Not Care" Conditions.
Proceedings of the Pattern Recognition and Machine Intelligence, 2015
Mining incomplete data with many attribute-concept values and "do not care" conditions.
Proceedings of the 2015 IEEE International Conference on Big Data (IEEE BigData 2015), Santa Clara, CA, USA, October 29, 2015
2014
Proceedings of the Issues and Challenges in Artificial Intelligence, 2014
Mining incomplete data with singleton, subset and concept probabilistic approximations.
Inf. Sci., 2014
Int. J. Approx. Reason., 2014
An Analysis of Probabilistic Approximations for Rule Induction from Incomplete Data Sets.
Fundam. Informaticae, 2014
A Comparison of Two Versions of the MLEM2 Rule Induction Algorithm Extended to Probabilistic Approximations.
Proceedings of the Rough Sets and Current Trends in Computing, 2014
Complexity of Rule Sets Induced from Incomplete Data Sets Using Global Probabilistic Approximations.
Proceedings of the Information Processing and Management of Uncertainty in Knowledge-Based Systems, 2014
Proceedings of the Hybrid Artificial Intelligence Systems - 9th International Conference, 2014
Proceedings of the 2014 IEEE International Conference on Granular Computing, 2014
Complexity of Rule Sets Induced from Incomplete Data with Attribute-concept Values and "Do Not Care" Conditions.
Proceedings of the DATA 2014, 2014
2013
An Experimental Comparison of Three Probabilistic Approximations Used for Rule Induction.
Fundam. Informaticae, 2013
Proceedings of the Rough Sets and Knowledge Technology - 8th International Conference, 2013
An Experimental Comparison of Three Interpretations of Missing Attribute Values Using Probabilistic Approximations.
Proceedings of the Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, 2013
A Comparison of global and local probabilistic approximations in mining data with many missing attribute values.
Proceedings of the 2013 IEEE International Conference on Granular Computing, 2013
Proceedings of the DATA 2013 - Proceedings of the 2nd International Conference on Data Technologies and Applications, Reykjavík, Iceland, 29, 2013
2012
How Good Are Probabilistic Approximations for Rule Induction from Data with Missing Attribute Values?
Proceedings of the Rough Sets and Current Trends in Computing, 2012
Proceedings of the Foundations of Intelligent Systems - 20th International Symposium, 2012
Experiments on rule induction from incomplete data using three probabilistic approximations.
Proceedings of the 2012 IEEE International Conference on Granular Computing, 2012
2011
Applied Artificial Intelligence Techniques for Identifying the Lazy Eye Vision Disorder.
J. Intell. Syst., 2011
Proceedings of the 2011 IEEE International Conference on Granular Computing, 2011