{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T14:15:28Z","timestamp":1777644928621,"version":"3.51.4"},"reference-count":0,"publisher":"SAGE Publications","issue":"1-4","license":[{"start":{"date-parts":[[2013,1,1]],"date-time":"2013-01-01T00:00:00Z","timestamp":1356998400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Fundamenta Informaticae"],"published-print":{"date-parts":[[2013,9]]},"abstract":"<jats:p>In this paper we present results of experiments on 166 incomplete data sets using three probabilistic approximations: lower, middle, and upper. Two interpretations of missing attribute values were used: lost and \u201cdo not care\u201d conditions. Our main objective was to select the best combination of an approximation and a missing attribute interpretation. We conclude that the best approach depends on the data set. The additional objective of our research was to study the average number of distinct probabilities associated with characteristic sets for all concepts of the data set. This number is much larger for data sets with \u201cdo not care\u201d conditions than with data sets with lost values. Therefore, for data sets with \u201cdo not care\u201d conditions the number of probabilistic approximations is also larger.<\/jats:p>","DOI":"10.3233\/fi-2013-903","type":"journal-article","created":{"date-parts":[[2019,12,3]],"date-time":"2019-12-03T00:34:47Z","timestamp":1575333287000},"page":"177-191","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["An Experimental Comparison of Three Probabilistic Approximations Used for Rule Induction"],"prefix":"10.1177","volume":"127","author":[{"given":"Patrick G.","family":"Clark","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA. pclark@ku.edu"}],"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 Institute of Computer Science, Polish Academy of Sciences, 01-237 Warsaw, Poland. jerzy@ku.edu"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2013,1]]},"container-title":["Fundamenta Informaticae"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/FI-2013-903","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/FI-2013-903","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T06:30:46Z","timestamp":1777444246000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/FI-2013-903"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,1]]},"references-count":0,"journal-issue":{"issue":"1-4","published-print":{"date-parts":[[2013,9]]}},"alternative-id":["10.3233\/FI-2013-903"],"URL":"https:\/\/doi.org\/10.3233\/fi-2013-903","relation":{},"ISSN":["0169-2968","1875-8681"],"issn-type":[{"value":"0169-2968","type":"print"},{"value":"1875-8681","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,1]]}}}