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Title
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Mining frequent itemsets in a stream
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Author
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Abstract
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| Mining frequent itemsets in a datastream proves to be a difficult problem, as itemsets arrive in rapid succession and storing parts of the stream is typically impossible. Nonetheless, it has many useful applications; e.g., opinion and sentiment analysis from social networks. Current stream mining algorithms are based on approximations. In earlier work, mining frequent items in a stream under the max-frequency measure proved to be effective for items. In this paper, we extended our work from items to itemsets. Firstly, an optimized incremental algorithm for mining frequent itemsets in a stream is presented. The algorithm maintains a very compact summary of the stream for selected itemsets. Secondly, we show that further compacting the summary is non-trivial. Thirdly, we establish a connection between the size of a summary and results from number theory. Fourthly, we report results of extensive experimentation, both of synthetic and real-world datasets, showing the efficiency of the algorithm both in terms of time and space. (C) 2012 Elsevier Ltd. All rights reserved. |
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Language
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English
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Source (journal)
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Information systems. - London
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Publication
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London
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2014
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ISSN
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0306-4379
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DOI
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10.1016/J.IS.2012.01.005
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Volume/pages
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39
(2014)
, p. 233-255
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ISI
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000329531300012
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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