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For rule induction, we use characteristic sets and generalized maximal consistent blocks. Therefore, we apply six different approaches for data mining. As follows from our previous experiments, where we used an error rate evaluated by ten-fold cross validation as the main criterion of quality, no approach is universally the best. Thus, we decided to compare our six approaches using complexity of rule sets induced from incomplete data sets. We show that the smallest rule sets are induced from incomplete data sets with attribute-concept values, while the most complicated rule sets are induced from data sets with lost values. The choice between interpretations of missing attribute values is more important than the choice between characteristic sets and generalized maximal consistent blocks.<\/jats:p>","DOI":"10.1093\/jigpal\/jzaa041","type":"journal-article","created":{"date-parts":[[2020,9,17]],"date-time":"2020-09-17T12:57:52Z","timestamp":1600347472000},"page":"124-137","source":"Crossref","is-referenced-by-count":5,"title":["Complexity of rule sets in mining incomplete data using characteristic sets and generalized maximal consistent blocks"],"prefix":"10.1093","volume":"29","author":[{"given":"Patrick G","family":"Clark","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jerzy W","family":"Grzymala-Busse","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA and Department of Expert Systems and Artificial Intelligence, University of Information Technology and Management, 35-225 Rzeszow, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Teresa","family":"Mroczek","sequence":"additional","affiliation":[{"name":"Department of Expert Systems and Artificial Intelligence, University of Information Technology and Management, 35-225 Rzeszow, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rafal","family":"Niemiec","sequence":"additional","affiliation":[{"name":"Department of Expert Systems and Artificial Intelligence, University of Information Technology and Management, 35-225 Rzeszow, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,9,18]]},"reference":[{"key":"2021032210353754500_ref1","first-page":"477","article-title":"Characteristic sets and generalized maximal consistent blocks in mining incomplete data","volume-title":"Proceedings of the International Joint Conference on Rough Sets, Part 1","author":"Clark","year":"2017"},{"key":"2021032210353754500_ref2","first-page":"84","article-title":"Complexity of rule sets in mining incomplete data using characteristic sets and generalized maximal consistent blocks","volume-title":"Proceedings of HAIS 2018, the 14th International Conference on Hybrid Artificial Intelligence Systems","author":"Clark","year":"2018"},{"key":"2021032210353754500_ref3","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1109\/GRC.2011.6122583","article-title":"Experiments on probabilistic approximations","volume-title":"Proceedings of the 2011 IEEE International Conference on Granular Computing","author":"Clark","year":"2011"},{"key":"2021032210353754500_ref4","first-page":"72","article-title":"Experiments using three probabilistic approximations for rule induction from incomplete data sets","volume-title":"Proceeedings of the MCCSIS 2012, IADIS European Conference on Data Mining ECDM 2012","author":"Clark","year":"2012"},{"key":"2021032210353754500_ref5","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-94-015-7975-9_1","article-title":"LERS\u2014a system for learning from examples based on rough sets","volume-title":"Intelligent Decision Support. 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