{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:38:59Z","timestamp":1750307939619,"version":"3.41.0"},"reference-count":45,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2007,8,1]],"date-time":"2007-08-01T00:00:00Z","timestamp":1185926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2007,8]]},"abstract":"<jats:p>\n            We investigate the general model of mining associations in a temporal database, where the exhibition periods of items are allowed to be different from one to another. The database is divided into partitions according to the time granularity imposed. Such temporal association rules allow us to observe short-term but interesting patterns that are absent when the whole range of the database is evaluated altogether. Prior work may omit some temporal association rules and thus have limited practicability. To remedy this and to give more precise frequent exhibition periods of frequent temporal itemsets, we devise an efficient algorithm\n            <jats:italic>Twain<\/jats:italic>\n            (standing for\n            <jats:italic>TWo end AssocIation miNer<\/jats:italic>\n            .)\n            <jats:italic>Twain<\/jats:italic>\n            not only generates frequent patterns with more precise frequent exhibition periods, but also discovers more interesting frequent patterns.\n            <jats:italic>Twain<\/jats:italic>\n            employs Start time and End time of each item to provide precise frequent exhibition period while progressively handling itemsets from one partition to another. Along with one scan of the database,\n            <jats:italic>Twain<\/jats:italic>\n            can generate frequent 2-itemsets directly according to the cumulative filtering threshold. Then,\n            <jats:italic>Twain<\/jats:italic>\n            adopts the scan reduction technique to generate all frequent\n            <jats:italic>k<\/jats:italic>\n            -itemsets (\n            <jats:italic>k<\/jats:italic>\n            &gt; 2) from the generated frequent 2-itemsets. Theoretical properties of\n            <jats:italic>Twain<\/jats:italic>\n            are derived as well in this article. The experimental results show that\n            <jats:italic>Twain<\/jats:italic>\n            outperforms the prior works in the quality of frequent patterns, execution time, I\/O cost, CPU overhead and scalability.\n          <\/jats:p>","DOI":"10.1145\/1267066.1267069","type":"journal-article","created":{"date-parts":[[2007,9,14]],"date-time":"2007-09-14T13:44:55Z","timestamp":1189777495000},"page":"8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Twain"],"prefix":"10.1145","volume":"1","author":[{"given":"Jen-Wei","family":"Huang","sequence":"first","affiliation":[{"name":"National Taiwan University, Taipei, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bi-Ru","family":"Dai","sequence":"additional","affiliation":[{"name":"National Taiwan University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming-Syan","family":"Chen","sequence":"additional","affiliation":[{"name":"National Taiwan University, Taipei, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2007,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1006\/jpdc.2000.1693"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/170035.170072"},{"volume-title":"Proceedings of the 20th International Conference on Very Large Data Bases, 478--499","author":"Agrawal R.","key":"e_1_2_1_3_1","unstructured":"Agrawal , R. and Srikant , R . 1994. Fast algorithms for mining association rules in large databases . In Proceedings of the 20th International Conference on Very Large Data Bases, 478--499 . Agrawal, R. and Srikant, R. 1994. Fast algorithms for mining association rules in large databases. In Proceedings of the 20th International Conference on Very Large Data Bases, 478--499."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/335603.335770"},{"volume-title":"Proceedings of the 1st ACM-SIAM Conference on Data Mining. ACM","author":"Ayad A. M.","key":"e_1_2_1_5_1","unstructured":"Ayad , A. M. , El-Makky , N. M. , and Taha , Y . 2001. Incremental mining of constrained association rules . In Proceedings of the 1st ACM-SIAM Conference on Data Mining. ACM , New York. Ayad, A. M., El-Makky, N. M., and Taha, Y. 2001. Incremental mining of constrained association rules. In Proceedings of the 1st ACM-SIAM Conference on Data Mining. 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