{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T12:42:25Z","timestamp":1666010545915},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,17]]},"abstract":"<jats:p>In [1], Newman et al. introduced the Reduced Mutual Information (RMI), a measure of the similarity between two partitions of a set useful in clustering and community detection. The computation of RMI requires counting the amount of contingency tables with fixed row and column sums, a #P-complete problem, for which the authors suggest to use analytical approximations that work in general, but for other not so pathological cases these give highly inaccurate approximations. We propose a hybrid scheme based on combining existing Markov chain Monte Carlo methods with analytical approximations to make more accurate estimates of the number of contingency tables in all cases.<\/jats:p>","DOI":"10.3233\/faia220334","type":"book-chapter","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T12:27:04Z","timestamp":1666009624000},"source":"Crossref","is-referenced-by-count":0,"title":["Towards and Efficient Algorithm for Computing the Reduced Mutual Information"],"prefix":"10.3233","author":[{"given":"Mart\u00ed","family":"Renedo-Mirambell","sequence":"first","affiliation":[{"name":"Soft Computing Research Group (SOCO) at Intelligent Data Science and Artificial Intelligence Research Center, Department of Computer Sciences, Polytechnical University of Catalonia, Barcelona, Spain. marti.renedo@gmail.com, argimiro@cs.upc.edu"}]},{"given":"Argimiro","family":"Arratia","sequence":"additional","affiliation":[{"name":"Soft Computing Research Group (SOCO) at Intelligent Data Science and Artificial Intelligence Research Center, Department of Computer Sciences, Polytechnical University of Catalonia, Barcelona, Spain. marti.renedo@gmail.com, argimiro@cs.upc.edu"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Artificial Intelligence Research and Development"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220334","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T12:27:15Z","timestamp":1666009635000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220334"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220334","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,17]]}}}