{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T23:51:59Z","timestamp":1773359519588,"version":"3.50.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2012,7,19]],"date-time":"2012-07-19T00:00:00Z","timestamp":1342656000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2012,9]]},"DOI":"10.1007\/s11432-012-4638-z","type":"journal-article","created":{"date-parts":[[2012,7,19]],"date-time":"2012-07-19T11:07:52Z","timestamp":1342696072000},"page":"2008-2030","source":"Crossref","is-referenced-by-count":116,"title":["A new algorithm for fast mining frequent itemsets using N-lists"],"prefix":"10.1007","volume":"55","author":[{"given":"ZhiHong","family":"Deng","sequence":"first","affiliation":[]},{"given":"ZhongHui","family":"Wang","sequence":"additional","affiliation":[]},{"given":"JiaJian","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2012,7,19]]},"reference":[{"key":"4638_CR1","doi-asserted-by":"crossref","unstructured":"Han J W, Pei J, Yin Y W. Mining frequent itemsets without candidate generation. In: The 2000 ACM SIGMOD International Conference on Management of data (SIGMOD\u201900), New York, 2000. 1\u201312","DOI":"10.1145\/342009.335372"},{"key":"4638_CR2","doi-asserted-by":"crossref","unstructured":"Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases. In: The 1993 ACM SIGMOD International Conference on Management of Data (SIGMOD\u201993), Washington, 1993. 207\u2013216","DOI":"10.1145\/170035.170072"},{"key":"4638_CR3","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s10618-006-0059-1","volume":"15","author":"J. Han","year":"2007","unstructured":"Han J, Cheng H, Xin D, et al. Frequent itemset mining: current status and future directions. Data Min Knowl Discov, 2007, 15: 55\u201386","journal-title":"Data Min Knowl Discov"},{"key":"4638_CR4","first-page":"493","volume":"21","author":"E. Baralis","year":"2009","unstructured":"Baralis E, Cerquitelli T, Chiusano S. IMine: index support for item set mining. IEEE TKDE J, 2009, 21: 493\u2013506","journal-title":"IEEE TKDE J"},{"key":"4638_CR5","doi-asserted-by":"crossref","unstructured":"Zaki M J, Gouda K. Fast vertical mining using diffsets, In: The 9th ACM SIGKDD International Conference on. Knowledge Discovery and Data Mining (SIGKDD\u201903), Washington, 2003. 326\u2013335","DOI":"10.1145\/956755.956788"},{"key":"4638_CR6","doi-asserted-by":"crossref","first-page":"733","DOI":"10.2991\/ijcis.2010.3.6.4","volume":"3","author":"Z. H. Deng","year":"2010","unstructured":"Deng Z H, Wang Z H. A new fast vertical method for mining frequent itemsets. Int J Comput Intell Syst, 2010, 3: 733\u2013744","journal-title":"Int J Comput Intell Syst"},{"key":"4638_CR7","unstructured":"Agrawal R, Srikant R. Fast algorithm for mining Association rules. In: The 20th International Conference on Very Large Data Bases (VLDB\u201994), Santiago de Chile, 1994. 487\u2013499"},{"key":"4638_CR8","unstructured":"Savasere A, Omiecinski E, Navathe S. An efficient algorithm for mining association rules in large databases. In: The 21th International Conference on Very Large Data Bases (VLDB\u201995), Zurich, 1995. 432\u2013443"},{"key":"4638_CR9","doi-asserted-by":"crossref","unstructured":"Shenoy P, Haritsa J R, Sundarshan S, et al. Turbo-charging vertical mining of large databases. In: ACM International Conference on Management of Data and Symposium on Principles of Database Systems (SIGMOD\u201900), Dallas, 2000. 22\u201333","DOI":"10.1145\/335191.335376"},{"key":"4638_CR10","first-page":"372","volume":"12","author":"M. J. Zaki","year":"2000","unstructured":"Zaki M J. Scalable algorithms for association mining. IEEE TKDE J, 2000, 12: 372\u2013390","journal-title":"IEEE TKDE J"},{"key":"4638_CR11","unstructured":"Pei J, Han J, Lu H, et al. H-mine: Hyper-structure mining of frequent itemsets in large databases. In: IEEE International Conference on Data Mining (ICDM\u201901), San Jose, 2001. 441\u2013448"},{"key":"4638_CR12","first-page":"249","volume":"9","author":"G. Liu","year":"2004","unstructured":"Liu G, Lu H, Lou W, et al. Efficient mining of frequent itemsets using ascending frequency ordered prefix-tree. DMKD J, 2004, 9: 249\u2013274","journal-title":"DMKD J"},{"key":"4638_CR13","first-page":"1347","volume":"17","author":"G. Grahne","year":"2005","unstructured":"Grahne G, Zhu J. Fast algorithms for frequent itemset mining using FP-Trees. IEEE TKDE J, 2005, 17: 1347\u20131362","journal-title":"IEEE TKDE J"},{"key":"4638_CR14","first-page":"875","volume":"16","author":"Y. K. Woon","year":"2004","unstructured":"Woon Y K, Ng W K, Lim E P. A support-ordered trie for fast frequent itemset discovery. IEEE TKDE J, 2004, 16: 875\u2013879","journal-title":"IEEE TKDE J"},{"key":"4638_CR15","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1016\/S0031-3203(01)00028-0","volume":"34","author":"V. S. Ananthanarayana","year":"2001","unstructured":"Ananthanarayana V S, Murty N M, Subramanian D K. An incremental data mining algorithm for compact realization of prototypes. Pattern Recognit, 2001, 34: 2249\u20132251","journal-title":"Pattern Recognit"},{"key":"4638_CR16","doi-asserted-by":"crossref","unstructured":"Grust T. Accelerating xpath location steps, In: The 2002 ACM SIGMOD International Conference on Management of Data (SIGMOD\u201902), Madison, 2002. 109\u2013120","DOI":"10.1145\/564691.564705"},{"key":"4638_CR17","volume-title":"Algorithm design","author":"J. Kleinberg","year":"2005","unstructured":"Kleinberg J, Tardos E. Algorithm design. Boston: Addison Wesley, 2005"},{"key":"4638_CR18","doi-asserted-by":"crossref","unstructured":"Bayardo Jr R J. Efficiently mining long itemsets from databases. In: ACM SIGMOD International Conference on Management of Data (SIGMOD\u201998), Seattle, 1998. 85\u201393","DOI":"10.1145\/276305.276313"},{"key":"4638_CR19","first-page":"1490","volume":"17","author":"D. Burdick","year":"2005","unstructured":"Burdick D, Calimlim M, Flannick J, et al. Mafia: A maximal frequent itemset algorithm. IEEE TKDE J, 2005, 17: 1490\u20131504","journal-title":"IEEE TKDE J"},{"key":"4638_CR20","unstructured":"Wang J Y, Han J, Pei J. CLOSET+: Searching for the Best Strate-gies for Mining frequent closed itemsets. In: The 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD\u201903), Washington, 2003. 236\u2013245"},{"key":"4638_CR21","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.datak.2008.02.001","volume":"66","author":"A. J. T. Lee","year":"2008","unstructured":"Lee A J T, Wang C S, Weng W Y, et al. An efficient algorithm for mining closed inter-transaction itemsets. Data Knowle Eng, 2008, 66: 68\u201391","journal-title":"Data Knowle Eng"},{"key":"4638_CR22","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.datak.2006.01.006","volume":"60","author":"D. Xin","year":"2007","unstructured":"Xin D, Han J, Yan X, et al. On compressing frequent itemsets. Data Knowl Eng, 2007, 60: 5\u201329","journal-title":"Data Knowl Eng"},{"key":"4638_CR23","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/j.datak.2009.01.002","volume":"68","author":"H. Li","year":"2009","unstructured":"Li H, Chen H. Mining non-derivable frequent itemsets over data stream. Data Knowl Eng, 2009, 68: 481\u2013498","journal-title":"Data Knowl Eng"},{"key":"4638_CR24","doi-asserted-by":"crossref","unstructured":"Yao H, Hamilton H J, Butz C J. A foundational approach to mining itemset utilities from databases. In: The SIAM Data Mining (SDM\u201904), Florida, 2004. 482\u2013486","DOI":"10.1137\/1.9781611972740.51"},{"key":"4638_CR25","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1016\/j.datak.2005.10.004","volume":"59","author":"H. Yao","year":"2006","unstructured":"Yao H, Hamilton H J. Mining itemset utilities from transaction databases. Data Knowl Eng, 2006, 59: 603\u2013626","journal-title":"Data Knowl Eng"},{"key":"4638_CR26","first-page":"1053","volume":"20","author":"L. B. Cao","year":"2008","unstructured":"Cao L B, Zhao Y C, Zhang C Q. Mining impact-targeted activity itemsets in imbalanced data. IEEE TKDE, 2008, 20: 1053\u20131066","journal-title":"IEEE TKDE"},{"key":"4638_CR27","doi-asserted-by":"crossref","unstructured":"Chang J H, Lee W S. Finding recent frequent itemsets adaptively over online data streams. In: The 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD\u201903), Washington, 2003. 487\u2013492","DOI":"10.1145\/956750.956807"},{"key":"4638_CR28","doi-asserted-by":"crossref","first-page":"8850","DOI":"10.1016\/j.eswa.2010.06.012","volume":"37","author":"X. Li","year":"2010","unstructured":"Li X, Deng Z H. Mining frequent itemsets from network flows for monitoring network. Expert Syst Appl, 2010, 37: 8850\u20138860","journal-title":"Expert Syst Appl"},{"key":"4638_CR29","doi-asserted-by":"crossref","unstructured":"Agrawal R, Srikant R. Mining sequential itemsets. In: The 11th International Conference on Data Engineering (ICDE\u201995), Taiwan, 2003. 3\u201314","DOI":"10.1109\/ICDE.1995.380415"},{"key":"4638_CR30","unstructured":"Yan X, Han J. gSpan: Graph-based substructure pattern mining. In: The 2002 IEEE International Conference on Data Mining (ICDM\u201902), Maebashi, 2002. 721\u2013724"},{"key":"4638_CR31","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1109\/TSMCB.2010.2086060","volume":"41","author":"L. B. Cao","year":"2011","unstructured":"Cao L B, Zhang H F, Zhao Y C, et al. Combined mining: Discovering informative knowledge in complex data. IEEE Trans Syst Man Cybern Part B-Cybern, 2011, 41: 699\u2013712","journal-title":"IEEE Trans Syst Man Cybern Part B-Cybern"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-012-4638-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11432-012-4638-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-012-4638-z","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T22:10:42Z","timestamp":1743804642000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11432-012-4638-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,7,19]]},"references-count":31,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2012,9]]}},"alternative-id":["4638"],"URL":"https:\/\/doi.org\/10.1007\/s11432-012-4638-z","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,7,19]]}}}