{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T20:41:08Z","timestamp":1778272868449,"version":"3.51.4"},"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":[[2007,3,23]],"date-time":"2007-03-23T20:08:48Z","timestamp":1174680528000},"page":"251-262","source":"Crossref","is-referenced-by-count":35,"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,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xindong","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Vermont, Burlington VT 05405, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingquan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Vermont, Burlington VT 05405, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gong","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Vermont, Burlington VT 05405, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunnian","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing University of Technology, Beijing 100022, P.R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2007,6,1]]},"reference":[{"key":"atypb1","volume-title":"Proceedings. of the 20th International Conference on very Large Databases (VLDB'94)","author":"R. Agrawal"},{"key":"atypb2","volume-title":"Proceedings of SIGMOD\/PODS","author":"B. Babcock"},{"key":"atypb3","volume-title":"Proceedings of the 2003 Workshop on Management and Processing of Data Streams (MPDS 2003)","author":"G. Dong"},{"key":"atypb4","volume-title":"Data mining trends and developments: the key data mining technologies and applications for the 21st century","author":"J. Hsu","year":"2002"},{"key":"atypb5","volume-title":"Proceedings of the ACM SIGMOD International Conference on Management of Data","author":"R. Agrawal"},{"key":"atypb6","volume-title":"Proceedings of the 7th International Conference on Database Theory","author":"N. Pasquier"},{"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"},{"key":"atypb10","volume-title":"Proceedings of the 29th VLDB Conference","author":"W. Teng"},{"key":"atypb11","volume-title":"1st International Workshop on Knowledge Discovery in Data Streams","author":"H. Li"},{"key":"atypb12","doi-asserted-by":"publisher","DOI":"10.1080\/088395101750065732"},{"key":"atypb13","volume-title":"Proceedings of 4th IEEE International Conference on Data Mining","author":"Y. Chi"},{"key":"atypb14","volume-title":"Proceedings of the 28th VLDB Conference","author":"G.S. Manku"},{"key":"atypb15","volume-title":"Mining frequent itemsets over arbitrary time intervals in data streams","author":"C. Giannella","year":"2003"},{"key":"atypb16","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCA.2002.804784"},{"key":"atypb17","volume-title":"Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2003)","author":"J. Chang"},{"key":"atypb18","volume-title":"Proceedings of the 2002 International Conference on Data Mining (ICDM'02)","author":"T. Asai"}],"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":[[2026,4,29]],"date-time":"2026-04-29T23:07:15Z","timestamp":1777504035000},"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":[{"value":"0165-5515","type":"print"},{"value":"1741-6485","type":"electronic"}],"subject":[],"published":{"date-parts":[[2007,6]]}}}