{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:29:22Z","timestamp":1750307362856,"version":"3.41.0"},"reference-count":29,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2010,10,1]],"date-time":"2010-10-01T00:00:00Z","timestamp":1285891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100004870","name":"Research Foundation of The City University of New York","doi-asserted-by":"publisher","award":["62274-40"],"award-info":[{"award-number":["62274-40"]}],"id":[{"id":"10.13039\/100004870","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2010,10]]},"abstract":"<jats:p>\n            The performance of a depth-first frequent itemset (FI) miming algorithm is closely related to the total number of recursions. In previous approaches this is mainly decided by the total number of FIs, which results in poor performance when a large number of FIs are involved. To solve this problem, a three-strategy adaptive algorithm, bitmap itemset support counting (BISC), is presented. The core strategy, BISC1, is used in the innermost steps of the recursion. For a database\n            <jats:italic>D<\/jats:italic>\n            with only\n            <jats:italic>s<\/jats:italic>\n            frequent items, a depth-first approach need up to\n            <jats:italic>s<\/jats:italic>\n            levels of recursions to detect all the FIs (up to 2\n            <jats:italic>\n              <jats:sup>s<\/jats:sup>\n            <\/jats:italic>\n            ). BISC1 completely replaces these recursions with a special summation that directly calculates the supports of all the possible 2\n            <jats:italic>\n              <jats:sup>s<\/jats:sup>\n            <\/jats:italic>\n            candidate itemsets. With BISC1 the run-time is entirely independent of the database after one database scan, and the per-candidate cost is only\n            <jats:italic>s<\/jats:italic>\n            . To offset the exponential growth of cost (both time and space) with BISC1 as\n            <jats:italic>s<\/jats:italic>\n            increases, a second strategy, BISC2, is introduced to effectively double the acceptable range of\n            <jats:italic>s<\/jats:italic>\n            . BISC2 divides an itemset into prefix and suffix and improves the performance by pruning all the itemsets with infrequent prefixes. If the total number of frequent items in\n            <jats:italic>D<\/jats:italic>\n            is high, the classic database projection strategy is used. In this case for the first\n            <jats:italic>s<\/jats:italic>\n            items a single run of BISC (1 or 2) is applied. For each of the remaining items, a projected database is created and the mining process proceeds recursively. To achieve optimal performance, BISC adaptively decides which strategy to use based on the dataset and minimum support. Experiments show that BISC outperforms previous approaches in all the datasets tested. Even though this does not guarantee that BISC will always perform the best, the result is impressive given the fact that most existing algorithms are only efficient in some types of datasets. The memory usage of BISC is also comparable to those of other algorithms.\n          <\/jats:p>","DOI":"10.1145\/1839490.1839493","type":"journal-article","created":{"date-parts":[[2010,10,19]],"date-time":"2010-10-19T12:36:24Z","timestamp":1287491784000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["BISC"],"prefix":"10.1145","volume":"4","author":[{"given":"Jinlin","family":"Chen","sequence":"first","affiliation":[{"name":"Queens College, City University of New York, Flushing, NY"}]},{"given":"Keli","family":"Xiao","sequence":"additional","affiliation":[{"name":"Rutgers, The State University of New Jersey, Newark, NJ"}]}],"member":"320","published-online":{"date-parts":[[2010,10,22]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/170035.170072"},{"volume-title":"Proceedings of 20th International Conference on Very Large Data Bases. Morgan Kaufmann, 487--499","author":"Agrawal R.","key":"e_1_2_1_2_1"},{"volume-title":"Proceedings ICDE'95","author":"Agrawal R.","key":"e_1_2_1_3_1"},{"volume-title":"Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations.","year":"2003","author":"Bodon F.","key":"e_1_2_1_4_1"},{"key":"e_1_2_1_5_1","unstructured":"Bodon F. 2006. A Survey on Frequent Itemset Mining Tech. rep. Budapest University of Technology and Economic http:\/\/www.cs.bme.hu\/~bodon\/kozos\/papers\/fim-survey.pdf.  Bodon F. 2006. A Survey on Frequent Itemset Mining Tech. rep. Budapest University of Technology and Economic http:\/\/www.cs.bme.hu\/~bodon\/kozos\/papers\/fim-survey.pdf."},{"volume-title":"Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations.","year":"2004","author":"Borgelt C.","key":"e_1_2_1_6_1"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/253262.253325"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1132956.1132958"},{"volume-title":"Proceedings of the ICDM Workshop on Frequent Itemset Mining Implementations.","author":"Grahne G.","key":"e_1_2_1_9_1"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.166"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335372"},{"volume-title":"Proceedings of the ICDM Workshop on Frequent Itemset Mining Implementations.","author":"Liu G.","key":"e_1_2_1_12_1"},{"volume-title":"Proceedings of the International Conference on Data Warehousing and Knowledge Discovery. 71--82","author":"Orlando S.","key":"e_1_2_1_13_1"},{"volume-title":"Proceedings of the IEEE International Conference on Data Mining, 338--345","author":"Orlando S.","key":"e_1_2_1_14_1"},{"volume-title":"Proceedings of the IEEE ICDM Workshop Frequent Itemset Mining Implementations, CEUR Workshop.","author":"Orlando S.","key":"e_1_2_1_15_1"},{"volume-title":"Proceedings of the 10th Turkish Symposium on Artificial Intelligence and Neural Networks, 257--264","author":"Ozel S. A.","key":"e_1_2_1_16_1"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/69.634757"},{"volume-title":"Proceedings of IEEE International Conference on Data Mining, 441--448","author":"Pei J.","key":"e_1_2_1_18_1"},{"volume-title":"Proceedings of the ICDM Workshop on Frequent Itemset Mining Implementations.","author":"Pietracaprina A.","key":"e_1_2_1_19_1"},{"volume-title":"Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations.","year":"2004","author":"Racz B.","key":"e_1_2_1_20_1"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/1133905.1133911"},{"volume-title":"Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations.","year":"2004","author":"Schmidt-Thieme L.","key":"e_1_2_1_22_1"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2006.52"},{"volume-title":"Proceedings of the ICDM Workshop on Frequent Itemset Mining Implementations.","author":"Uno T.","key":"e_1_2_1_24_1"},{"volume-title":"InProceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations.","author":"Uno T.","key":"e_1_2_1_25_1"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/1133905.1133916"},{"volume-title":"Proceedings of the 6th Pacific Asia Conference on Knowledge Discovery and Data Mining, 334--340","author":"Wang K.","key":"e_1_2_1_27_1"},{"volume-title":"Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining. AAAI Press, 283--286","author":"Zaki M. J.","key":"e_1_2_1_28_1"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956788"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1839490.1839493","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/1839490.1839493","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T11:22:35Z","timestamp":1750245755000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1839490.1839493"}},"subtitle":["A bitmap itemset support counting approach for efficient frequent itemset mining"],"short-title":[],"issued":{"date-parts":[[2010,10]]},"references-count":29,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2010,10]]}},"alternative-id":["10.1145\/1839490.1839493"],"URL":"https:\/\/doi.org\/10.1145\/1839490.1839493","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2010,10]]},"assertion":[{"value":"2008-10-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2009-12-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2010-10-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}