{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T11:56:12Z","timestamp":1722254172495},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"02","license":[{"start":{"date-parts":[[2020,4,3]],"date-time":"2020-04-03T00:00:00Z","timestamp":1585872000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.aaai.org"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Making good decisions at the top of a search tree is important for finding good solutions early in constraint optimization. In this paper, we propose a method employing frequent pattern mining (FPM), a classic datamining technique, to find good subtrees for solving constraint optimization problems. We demonstrate that applying FPM in a small number of random high-quality feasible solutions enables us to identify subtrees containing optimal solutions in more than 55% of problem instances for four real world benchmark problems. The method works as a plugin that can be combined with any search strategy for branch-and-bound search. Exploring the identified subtrees first, the method brings substantial improvements for four efficient search strategies in both total runtime and runtime of finding optimal solutions.<\/jats:p>","DOI":"10.1609\/aaai.v34i02.5518","type":"journal-article","created":{"date-parts":[[2020,6,29]],"date-time":"2020-06-29T19:36:36Z","timestamp":1593459396000},"page":"1577-1584","source":"Crossref","is-referenced-by-count":1,"title":["Finding Good Subtrees for Constraint Optimization Problems Using Frequent Pattern Mining"],"prefix":"10.1609","volume":"34","author":[{"given":"Hongbo","family":"Li","sequence":"first","affiliation":[]},{"given":"Jimmy","family":"Lee","sequence":"additional","affiliation":[]},{"given":"He","family":"Mi","sequence":"additional","affiliation":[]},{"given":"Minghao","family":"Yin","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2020,4,3]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/5518\/5374","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/5518\/5374","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T23:48:40Z","timestamp":1667519320000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/5518"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,3]]},"references-count":0,"journal-issue":{"issue":"02","published-online":{"date-parts":[[2020,6,15]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v34i02.5518","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2020,4,3]]}}}