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Mining incomplete data with lost values and attribute-concept values.

, and . GrC, page 49-54. IEEE Computer Society, (2014)

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Complexity of Rule Sets Induced by Characteristic Sets and Generalized Maximal Consistent Blocks., , , , and . ICAISC (2), volume 10842 of Lecture Notes in Computer Science, page 301-310. Springer, (2018)A Comparison of Four Classification Systems Using Rule Sets Induced from Incomplete Data Sets by Local Probabilistic Approximations., , and . ISMIS, volume 10352 of Lecture Notes in Computer Science, page 282-291. Springer, (2017)Characteristic Sets and Generalized Maximal Consistent Blocks in Mining Incomplete Data., , , and . IJCRS (1), volume 10313 of Lecture Notes in Computer Science, page 477-486. Springer, (2017)A comparison of two MLEM2 rule induction algorithms extended to probabilistic approximations., , and . J. Intell. Inf. Syst., 47 (3): 515-529 (2016)Mining Data with Many Missing Attribute Values Using Global and Saturated Probabilistic Approximations Based on Characteristic Sets., , , and . ICIST, volume 1283 of Communications in Computer and Information Science, page 72-83. Springer, (2020)Experiments on probabilistic approximations., and . GrC, page 144-149. IEEE Computer Society, (2011)A Comparison of Mining Incomplete and Inconsistent Data., , and . ITC, 46 (2): 183-193 (2017)Rule Set Complexity for Incomplete Data Sets with Many Attribute-Concept Values and "Do Not Care" Conditions., , and . IJCRS, volume 9920 of Lecture Notes in Computer Science, page 65-74. (2016)Characteristic sets and generalized maximal consistent blocks in mining incomplete data., , , and . Inf. Sci., (2018)A Comparison of Characteristic Sets and Generalized Maximal Consistent Blocks in Mining Incomplete Data., , , and . IPMU (2), volume 854 of Communications in Computer and Information Science, page 480-489. Springer, (2018)