{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:27:30Z","timestamp":1777854450177,"version":"3.51.4"},"reference-count":26,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2011,2,28]],"date-time":"2011-02-28T00:00:00Z","timestamp":1298851200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Information Science"],"published-print":{"date-parts":[[2011,4]]},"abstract":"<jats:p>Mining inter-transaction association rules is one of the most interesting issues in data mining research. However, in a data stream environment the previous approaches are unable to find the result of the new-incoming data and the original database without re-computing the whole database. In this paper, we propose an incremental mining algorithm, called DSM-CITI (Data Stream Mining for Closed Inter-Transaction Itemsets), for discovering the set of all frequent inter-transaction itemsets from data streams. In the framework of DSM-CITI, a new in-memory summary data structure, ITP-tree, is developed to maintain frequent inter-transaction itemsets. Moreover, algorithm DSM-CITI is able to construct ITP-tree incrementally and uses the property to avoid unnecessary updates. Experimental studies show that the proposed algorithm is efficient and scalable for mining frequent inter-transaction itemsets over stream sliding windows.<\/jats:p>","DOI":"10.1177\/0165551511401539","type":"journal-article","created":{"date-parts":[[2011,3,1]],"date-time":"2011-03-01T16:30:41Z","timestamp":1298997041000},"page":"208-220","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":8,"title":["Incremental mining of closed inter-transaction itemsets over data stream                 sliding windows"],"prefix":"10.1177","volume":"37","author":[{"given":"Shih-Chuan","family":"Chiu","sequence":"first","affiliation":[{"name":"Department of Computer Science, National Chiao Tung University, Hsinchu                         300, Taiwan, ROC"}]},{"given":"Hua-Fu","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Information Management, Kainan University, Taoyuan 338,                         Taiwan, ROC"}]},{"given":"Jiun-Long","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National Chiao Tung University, Hsinchu                         300, Taiwan, ROC,"}]},{"given":"Hsin-Han","family":"You","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National Chiao Tung University, Hsinchu                         300, Taiwan, ROC"}]}],"member":"179","published-online":{"date-parts":[[2011,2,28]]},"reference":[{"key":"atypb1","volume-title":"Proceedings of ACM-SIGMOD Workshop on Research Issues on Data Mining and Knowledge, Seattle, USA","author":"H. 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