{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T16:36:13Z","timestamp":1742402173378,"version":"3.38.0"},"reference-count":18,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2007,6,1]],"date-time":"2007-06-01T00:00:00Z","timestamp":1180656000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Information Science"],"published-print":{"date-parts":[[2007,6]]},"abstract":"<jats:p> Frequent pattern mining from data streams is an active research topic in data mining. Existing research efforts often rely on a two-phase framework to discover frequent patterns: (1) using internal data structures to store meta-patterns obtained by scanning the stream data; and (2) re-mining the meta-patterns to finalize and output frequent patterns. The defectiveness of such a two-phase framework lies in the fact that the two stages provide barriers to dynamically and immediately finding frequent patterns with online functionalities. It is expected that a single-phase algorithm can fulfil frequent pattern mining from data streams in such a way that the users can see patterns in an immediate and dynamic manner, as soon as the patterns have become frequent. In this paper, we propose INSTANT, a single-phase algorithm for discovering frequent itemsets from data streams. The theoretical foundation of INSTANT is based on a framework theory on a set of itemsets, which is also presented in the paper. The novel design of INSTANT ensures that it employs compact data structures to mine frequent patterns from data streams in a single phase. Our experimental results demonstrate the time and space efficiency of the proposed algorithm. <\/jats:p>","DOI":"10.1177\/0165551506068179","type":"journal-article","created":{"date-parts":[[2007,3,24]],"date-time":"2007-03-24T00:08:48Z","timestamp":1174694928000},"page":"251-262","source":"Crossref","is-referenced-by-count":34,"title":["Mining maximal frequent itemsets from data streams"],"prefix":"10.1177","volume":"33","author":[{"given":"Guojun","family":"Mao","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Vermont, Burlington VT 05405, USA,"}]},{"given":"Xindong","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Vermont, Burlington VT 05405, USA"}]},{"given":"Xingquan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Vermont, Burlington VT 05405, USA"}]},{"given":"Gong","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Vermont, Burlington VT 05405, USA"}]},{"given":"Chunnian","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing University of Technology, Beijing 100022, P.R. China"}]}],"member":"179","published-online":{"date-parts":[[2007,6,1]]},"reference":[{"volume-title":"Proceedings. of the 20th International Conference on very Large Databases (VLDB'94)","author":"R. Agrawal","key":"atypb1"},{"volume-title":"Proceedings of SIGMOD\/PODS","author":"B. Babcock","key":"atypb2"},{"volume-title":"Proceedings of the 2003 Workshop on Management and Processing of Data Streams (MPDS 2003)","author":"G. Dong","key":"atypb3"},{"volume-title":"Data mining trends and developments: the key data mining technologies and applications for the 21st century","year":"2002","author":"J. Hsu","key":"atypb4"},{"volume-title":"Proceedings of the ACM SIGMOD International Conference on Management of Data","author":"R. Agrawal","key":"atypb5"},{"volume-title":"Proceedings of the 7th International Conference on Database Theory","author":"N. Pasquier","key":"atypb6"},{"key":"atypb7","unstructured":"J. Pei, J. Han and R. Mao, CLOSET: an efficient algorithm for mining frequent closed itemsets. In: SIGMOD'00 (ACM Press, Dallas, TX, 2000) 21\u201430."},{"key":"atypb8","first-page":"12","volume":"02","author":"M.J. Zaki","year":"2000","journal-title":"SDM'"},{"key":"atypb9","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335372"},{"volume-title":"Proceedings of the 29th VLDB Conference","author":"W. Teng","key":"atypb10"},{"volume-title":"1st International Workshop on Knowledge Discovery in Data Streams","author":"H. Li","key":"atypb11"},{"key":"atypb12","doi-asserted-by":"publisher","DOI":"10.1080\/088395101750065732"},{"volume-title":"Proceedings of 4th IEEE International Conference on Data Mining","author":"Y. Chi","key":"atypb13"},{"volume-title":"Proceedings of the 28th VLDB Conference","author":"G.S. Manku","key":"atypb14"},{"volume-title":"Mining frequent itemsets over arbitrary time intervals in data streams","year":"2003","author":"C. 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Asai","key":"atypb18"}],"container-title":["Journal of Information Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0165551506068179","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0165551506068179","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T11:52:51Z","timestamp":1740829971000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/0165551506068179"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2007,6]]},"references-count":18,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2007,6]]}},"alternative-id":["10.1177\/0165551506068179"],"URL":"https:\/\/doi.org\/10.1177\/0165551506068179","relation":{},"ISSN":["0165-5515","1741-6485"],"issn-type":[{"type":"print","value":"0165-5515"},{"type":"electronic","value":"1741-6485"}],"subject":[],"published":{"date-parts":[[2007,6]]}}}