{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T12:30:01Z","timestamp":1759667401338,"version":"3.38.0"},"reference-count":32,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2011,3,29]],"date-time":"2011-03-29T00:00:00Z","timestamp":1301356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Statistical Analysis"],"published-print":{"date-parts":[[2011,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Metaheuristics represent an important class of techniques to solve, approximately, hard combinatorial optimization problems for which the use of exact methods is impractical. In this work, we propose a hybrid version of the Greedy Randomized Adaptive Search Procedures (GRASP) metaheuristic, which incorporates a data mining process, to solve the<jats:italic>p<\/jats:italic>\u2010median problem. We believe that patterns obtained by a data mining technique, from a set of suboptimal solutions of a combinatorial optimization problem, can be used to guide metaheuristic procedures in the search for better solutions. Traditional GRASP is an iterative metaheuristic which returns the best solution reached over all iterations. In the hybrid GRASP proposal, after executing a significant number of iterations, the data mining process extracts patterns from an elite set of suboptimal solutions for the<jats:italic>p<\/jats:italic>\u2010median problem. These patterns present characteristics of near optimal solutions and can be used to guide the following GRASP iterations in the search through the combinatorial solution space. Computational experiments, comparing traditional GRASP and different data mining hybrid proposals for the<jats:italic>p<\/jats:italic>\u2010median problem, showed that employing patterns mined from an elite set of suboptimal solutions made the hybrid GRASP find better results. Besides, the conducted experiments also evidenced that incorporating a data mining technique into a metaheuristic accelerated the process of finding near optimal and optimal solutions. \u00a9 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 2011<\/jats:p>","DOI":"10.1002\/sam.10116","type":"journal-article","created":{"date-parts":[[2011,3,29]],"date-time":"2011-03-29T20:40:42Z","timestamp":1301431242000},"page":"313-335","source":"Crossref","is-referenced-by-count":19,"title":["A hybrid data mining metaheuristic for the<i>p<\/i>\u2010median problem"],"prefix":"10.1002","volume":"4","author":[{"given":"Alexandre","family":"Plastino","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard","family":"Fuchshuber","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simone de L.","family":"Martins","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alex A.","family":"Freitas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Said","family":"Salhi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2011,3,29]]},"reference":[{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF02125421"},{"key":"e_1_2_12_3_2","first-page":"38","volume-title":"Heuristic Search: The Science of Tomorrow, OR48 Keynote Papers","author":"Salhi S.","year":"2006"},{"key":"e_1_2_12_4_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1016540724870"},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1475-3995.2008.00644.x"},{"key":"e_1_2_12_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/0167-6377(89)90002-3"},{"key":"e_1_2_12_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF01096763"},{"key":"e_1_2_12_8_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1475-3995.2009.00663.x"},{"key":"e_1_2_12_9_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1475-3995.2009.00664.x"},{"volume-title":"Data Mining: Concepts and Techniques","year":"2006","author":"Han J.","key":"e_1_2_12_10_2"},{"volume-title":"Data Mining: Practical Machine Learning Tools and Techniques","year":"2005","author":"Witten I. H.","key":"e_1_2_12_11_2"},{"key":"e_1_2_12_12_2","unstructured":"M. H. F.Ribeiro V. F.Trindade A.Plastino andS. L.Martins Hybridization of GRASP metaheuristic with data mining techniques In Proceedings of the ECAI Workshop on Hybrid Metaheuristics Valencia Spain2004 69\u201378."},{"key":"e_1_2_12_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10852-005-9030-1"},{"key":"e_1_2_12_14_2","doi-asserted-by":"publisher","DOI":"10.1287\/opre.21.2.498"},{"key":"e_1_2_12_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0377-2217(98)00359-2"},{"key":"e_1_2_12_16_2","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.11.2.198"},{"key":"e_1_2_12_17_2","doi-asserted-by":"crossref","unstructured":"L. F.Santos M. H. F.Ribeiro A.Plastino andS. L.Martins A hybrid GRASP with data mining for the maximum diversity problem In Proceedings of the International Workshop on Hybrid Metaheuristics Barcelona Spain LNCS 3636 2005 116\u2013127.","DOI":"10.1007\/11546245_11"},{"key":"e_1_2_12_18_2","doi-asserted-by":"crossref","unstructured":"L. F.Santos C. V.Albuquerque S. L.Martins andA.Plastino A hybrid GRASP with data mining for efficient server replication for reliable multicast In Proceedings of the IEEE GLOBECOM Conference San Francisco California USA 2006.","DOI":"10.1109\/GLOCOM.2006.246"},{"key":"e_1_2_12_19_2","doi-asserted-by":"publisher","DOI":"10.1137\/0137041"},{"key":"e_1_2_12_20_2","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.29.4.482"},{"key":"e_1_2_12_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2005.05.034"},{"volume-title":"Greedy Randomized Adaptive Search Procedures, Handbook of Metaheuristics","year":"2003","author":"Resende M. G. C.","key":"e_1_2_12_22_2"},{"key":"e_1_2_12_23_2","unstructured":"R.AgrawalandR.Srikant Fast algorithms for mining association rules In Proceedings of the Very Large Data Bases Conference Santiago de Chile Chile 1994 487\u2013499."},{"key":"e_1_2_12_24_2","unstructured":"B.GoethalsandM. J.Zaki Advances in frequent itemset mining implementations: introduction to FIMI\u201003 In Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations Melbourne Florida USA 2003."},{"key":"e_1_2_12_25_2","doi-asserted-by":"crossref","unstructured":"J.Han J.Pei andY.Yin Mining frequent patterns without candidate generation Proceedings of the ACM SIGMOD International Conference on Management of Data Dallas Texas USA 2000 1\u201312.","DOI":"10.1145\/335191.335372"},{"key":"e_1_2_12_26_2","doi-asserted-by":"crossref","unstructured":"S.Orlando P.Palmerimi andR.Perego Adaptive and resource\u2010aware mining of frequent sets In Proceedings of the IEEE International Conference on Data Mining Maebashi City Japan 2002 338\u2013345.","DOI":"10.1109\/ICDM.2002.1183921"},{"key":"e_1_2_12_27_2","unstructured":"G.GrahneandJ.Zhu Efficiently using prefix\u2010trees in mining frequent item\u2010sets In Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations Melbourne Florida USA 2003."},{"key":"e_1_2_12_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/0377-2217(85)90040-2"},{"key":"e_1_2_12_29_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF01581151"},{"key":"e_1_2_12_30_2","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.3.4.376"},{"key":"e_1_2_12_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0305-0548(02)00095-3"},{"key":"e_1_2_12_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11590-006-0031-4"},{"key":"e_1_2_12_33_2","doi-asserted-by":"crossref","unstructured":"C. C.Ribeiro I.Rosseti andR.Vallejos On the use of run time distributions to evaluate and compare stochastic local search algorithms In Proceedings of the Engineering Stochastic Local Search Algorithms Workshop Lecture Notes in Computer Science 5752 Brussels Belgium 2009 16\u201330.","DOI":"10.1007\/978-3-642-03751-1_2"}],"container-title":["Statistical Analysis and Data Mining: The ASA Data Science Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Fsam.10116","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/sam.10116","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T18:56:35Z","timestamp":1741114595000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/onlinelibrary.wiley.com\/doi\/10.1002\/sam.10116"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011,3,29]]},"references-count":32,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2011,6]]}},"alternative-id":["10.1002\/sam.10116"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/sam.10116","archive":["Portico"],"relation":{},"ISSN":["1932-1864","1932-1872"],"issn-type":[{"type":"print","value":"1932-1864"},{"type":"electronic","value":"1932-1872"}],"subject":[],"published":{"date-parts":[[2011,3,29]]}}}