{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T09:14:14Z","timestamp":1648631654569},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2014,8,10]],"date-time":"2014-08-10T00:00:00Z","timestamp":1407628800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2016,10]]},"DOI":"10.1007\/s13042-014-0290-9","type":"journal-article","created":{"date-parts":[[2014,8,9]],"date-time":"2014-08-09T18:56:56Z","timestamp":1407610616000},"page":"741-751","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Speeding up maximal fully-correlated itemsets search in large databases"],"prefix":"10.1007","volume":"7","author":[{"given":"Lian","family":"Duan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"W. Nick","family":"Street","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2014,8,10]]},"reference":[{"key":"290_CR1","doi-asserted-by":"crossref","unstructured":"Agrawal R, Imieli\u0144ski T, Swami A (1993) Mining association rules between sets of items in large databases. In: SIGMOD \u201993: Proceedings of the ACM SIGMOD international conference on management of data. ACM, New York, pp 207\u2013216","DOI":"10.1145\/170035.170072"},{"key":"290_CR2","doi-asserted-by":"crossref","unstructured":"Bayardo RJ, Jr. (1998) Efficiently mining long patterns from databases. In: SIGMOD \u201998: Proceedings of the 1998 ACM SIGMOD international conference on management of data. ACM, New York, pp 85\u201393","DOI":"10.1145\/276304.276313"},{"key":"290_CR3","doi-asserted-by":"crossref","unstructured":"Brin S, Motwani R, Silverstein C (1997) Beyond market baskets: Generalizing association rules to correlations. In: SIGMOD \u201997: Proceedings ACM SIGMOD international conference on management of data. ACM, New York, pp 265\u2013276","DOI":"10.1145\/253260.253327"},{"key":"290_CR4","doi-asserted-by":"crossref","unstructured":"Brin S, Motwani R, Ullman JD, Tsur S (1997) Dynamic itemset counting and implication rules for market basket data. In: SIGMOD \u201997: Proceedings of the ACM SIGMOD international conference on management of data. ACM, New York, pp 255\u2013264","DOI":"10.1145\/253260.253325"},{"key":"290_CR5","doi-asserted-by":"crossref","unstructured":"Burdick D (2001) Mafia: A maximal frequent itemset algorithm for transactional databases. In: ICDE \u201901: Proceedings of the 17th international conference on data engineering. IEEE Computer Society, Washington, DC, p 443","DOI":"10.1109\/ICDE.2001.914857"},{"issue":"2","key":"290_CR6","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1109\/TITB.2012.2227272","volume":"17","author":"L Duan","year":"2013","unstructured":"Duan L, Khoshneshin M, Street W, Liu M (2013) Adverse drug effect detection. IEEE J Biomed Health Inf 17(2):305\u2013311","journal-title":"IEEE J Biomed Health Inf"},{"key":"290_CR7","doi-asserted-by":"crossref","unstructured":"Duan L, Street WN (2009) Finding maximal fully-correlated itemsets in large databases. In: ICDM \u201909: Proceedings of the 9th international conference on data mining. IEEE Computer Society, Miami, pp 770\u2013775","DOI":"10.1109\/ICDM.2009.89"},{"issue":"13","key":"290_CR8","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1016\/j.patrec.2013.05.027","volume":"34","author":"L Duan","year":"2013","unstructured":"Duan L, Street WN, Liu Y (2013) Speeding up correlation search for binary data. Pattern Recognit Lett 34(13):1499\u20131507","journal-title":"Pattern Recognit Lett"},{"key":"290_CR9","doi-asserted-by":"crossref","unstructured":"Duan L, Street WN, Liu Y, Lu H (2014) Community detection in graphs through correlation. In: The 20th ACM SIGKDD conference on knowledge discovery and data mining (accepted)","DOI":"10.1145\/2623330.2623629"},{"key":"290_CR10","doi-asserted-by":"crossref","unstructured":"Duan L, Street WN, Liu Y, Xu S, Wu B (2014) Selecting the right correlation measure for binary data. ACM transactions on knowledge discovery from data (accepted)","DOI":"10.1145\/2637484"},{"issue":"1","key":"290_CR11","first-page":"61","volume":"19","author":"T Dunning","year":"1993","unstructured":"Dunning T (1993) Accurate methods for the statistics of surprise and coincidence. Comput Linguist 19(1):61\u201374","journal-title":"Comput Linguist"},{"issue":"3","key":"290_CR12","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1145\/1132960.1132963","volume":"38","author":"L Geng","year":"2006","unstructured":"Geng L, Hamilton HJ (2006) Interestingness measures for data mining: A survey. ACM Comput Surv 38(3):9","journal-title":"ACM Comput Surv"},{"issue":"3","key":"290_CR13","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1007\/s10618-005-0002-x","volume":"11","author":"K Gouda","year":"2005","unstructured":"Gouda K, Zaki MJ (2005) Genmax: An efficient algorithm for mining maximal frequent itemsets. Data Min Knowl Discov 11(3):223\u2013242","journal-title":"Data Min Knowl Discov"},{"key":"290_CR14","doi-asserted-by":"crossref","unstructured":"Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: SIGMOD \u201900: Proceedings of the 2000 ACM SIGMOD international conference on management of data. ACM, New York, pp 1\u201312","DOI":"10.1145\/342009.335372"},{"issue":"1","key":"290_CR15","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.is.2003.08.004","volume":"30","author":"C Jermaine","year":"2005","unstructured":"Jermaine C (2005) Finding the most interesting correlations in a database: How hard can it be? Inf Syst 30(1):21\u201346","journal-title":"Inf Syst"},{"issue":"3","key":"290_CR16","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1136\/amiajnl-2012-001119","volume":"20","author":"M Liu","year":"2013","unstructured":"Liu M, Hinz ERM, Matheny ME, Denny JC, Schildcrout JS, Miller RA, Xu H (2013) Comparative analysis of pharmacovigilance methods in the detection of adverse drug reactions using electronic medical records. J Am Med Inform Assoc 20(3):420\u2013426","journal-title":"J Am Med Inform Assoc"},{"key":"290_CR17","doi-asserted-by":"publisher","unstructured":"Mohamed MH, Darwieesh MM (2013) Efficient mining frequent itemsets algorithms. Int J Mach Learn Cybern. doi: 10.1007\/s13042-013-0172-6","DOI":"10.1007\/s13042-013-0172-6"},{"issue":"1","key":"290_CR18","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1109\/TKDE.2003.1161582","volume":"15","author":"ER Omiecinski","year":"2003","unstructured":"Omiecinski ER (2003) Alternative interest measures for mining associations in databases. IEEE Trans Knowl Data Eng 15(1):57\u201369","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"290_CR19","volume-title":"Discovery, analysis, and presentation of strong rules","author":"G Piatetsky-Shapiro","year":"1991","unstructured":"Piatetsky-Shapiro G (1991) Discovery, analysis, and presentation of strong rules. AAAI\/MIT Press, Cambridge"},{"issue":"4","key":"290_CR20","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/S0306-4379(03)00072-3","volume":"29","author":"P-N Tan","year":"2004","unstructured":"Tan P-N, Kumar V, Srivastava J (2004) Selecting the right objective measure for association analysis. Inf Syst 29(4):293\u2013313","journal-title":"Inf Syst"},{"key":"290_CR21","doi-asserted-by":"crossref","unstructured":"Tew C, Giraud-Carrier C, Tanner K, Burton S (2014) Behavior-based clustering and analysis of interestingness measures for association rule mining. Data Min Knowl Discov 28(4):1004\u20131045","DOI":"10.1007\/s10618-013-0326-x"},{"key":"290_CR22","doi-asserted-by":"publisher","unstructured":"Vo B, Le T, Coenen F, Hong T-P (2014) Mining frequent itemsets using the n-list and subsume concepts. Int J Mach Learn Cybern. doi: 10.1007\/s13042-014-0252-2","DOI":"10.1007\/s13042-014-0252-2"},{"key":"290_CR23","doi-asserted-by":"publisher","unstructured":"Zhou W, Zhang H (2013) Correlation range query for effective recommendations. World Wide Web. doi: 10.1007\/s11280-013-0265-x","DOI":"10.1007\/s11280-013-0265-x"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-014-0290-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13042-014-0290-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-014-0290-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-014-0290-9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,2]],"date-time":"2019-06-02T14:31:16Z","timestamp":1559485876000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13042-014-0290-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,8,10]]},"references-count":23,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2016,10]]}},"alternative-id":["290"],"URL":"https:\/\/doi.org\/10.1007\/s13042-014-0290-9","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,8,10]]}}}