{"id":9308,"date":"2018-03-15T12:09:22","date_gmt":"2018-03-15T12:09:22","guid":{"rendered":"http:\/\/cio.edu.umh.es\/?p=9308"},"modified":"2018-03-15T12:09:22","modified_gmt":"2018-03-15T12:09:22","slug":"a-novel-characterisation-based-algorithm-to-discover-new-knowledge-from-classification-datasets-without-use-of-support","status":"publish","type":"post","link":"https:\/\/cio.umh.es\/en\/2018\/03\/15\/a-novel-characterisation-based-algorithm-to-discover-new-knowledge-from-classification-datasets-without-use-of-support\/","title":{"rendered":"A novel characterisation-based algorithm to discover new knowledge from classification datasets without use of support"},"content":{"rendered":"<p>[:es]<span style=\"color: #000000\">Lazcorreta Puigmart\u00ed, E. (Universidad Miguel Hern\u00e1ndez); Botella, F. (Universidad Miguel Hern\u00e1ndez); Fern\u00e1ndez-Caballero, A (Universidad Castilla- La Mancha)<\/span><br \/>\n<span style=\"color: #000000\">Abstract: This paper introduces a novel proposal to discover the best associative classification rules through studying the influence of the attributes used in robust catalogues. Notice that a catalogue is defined as a dataset free of duplicate records. Moreover, a robust catalogue is obtained when incomplete records and those with uncertainty are eliminated from a catalogue. Therefore, a robust catalogue is a collection of association rules with 100% confidence and unitary support. In this paper we demonstrate that robust catalogues contain the same association rules as the datasets from which they were obtained, but can be managed in memory without eliminating any data from the analysis. In fact, the experiments performed show that all robust catalogues contained in a classification dataset are easily obtained, providing millions of associative classification rules with 100% confidence to the expert researcher in data mining. \u00a9 2017 Elsevier Ltd<\/span>[:]<\/p>","protected":false},"excerpt":{"rendered":"<p>[:es]Lazcorreta Puigmart\u00ed, E. (Universidad Miguel Hern\u00e1ndez); Botella, F. (Universidad Miguel Hern\u00e1ndez); Fern\u00e1ndez-Caballero, A (Universidad Castilla- La Mancha)<br \/>\nAbstract: This paper introduces a novel proposal to discover the best associative classification rules through studying the influence of the attributes used in robust catalogues. Notice that a catalogue is defined as a dataset free of duplicate records. Moreover, [&#8230;]<\/p>","protected":false},"author":3477,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_links_to":"","_links_to_target":""},"categories":[369888],"tags":[],"_links":{"self":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/posts\/9308"}],"collection":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/users\/3477"}],"replies":[{"embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/comments?post=9308"}],"version-history":[{"count":0,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/posts\/9308\/revisions"}],"wp:attachment":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/media?parent=9308"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/categories?post=9308"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/tags?post=9308"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}