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In this article, we therefore address the problem of mining rank data, that is, data in the form of rankings (total orders) of an underlying set of items. More specifically, two types of patterns are considered, namely frequent rankings and dependencies between such rankings in the form of association rules. Algorithms for mining frequent rankings and frequent closed rankings are proposed and tested experimentally, using both synthetic and real data.<\/jats:p>","DOI":"10.1145\/3363572","type":"journal-article","created":{"date-parts":[[2019,11,12]],"date-time":"2019-11-12T21:41:21Z","timestamp":1573594881000},"page":"1-36","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Mining Rank Data"],"prefix":"10.1145","volume":"13","author":[{"given":"Sascha","family":"Henzgen","sequence":"first","affiliation":[{"name":"Paderborn University, Paderborn, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eyke","family":"H\u00fcllermeier","sequence":"additional","affiliation":[{"name":"Paderborn University, Paderborn, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,11,11]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"C. 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