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2002
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Online Algorithms for Mining Semi-structured Data Stream

10 years 6 months ago
Online Algorithms for Mining Semi-structured Data Stream
In this paper, we study an online data mining problem from streams of semi-structured data such as XML data. Modeling semi-structured data and patterns as labeled ordered trees, we present an online algorithm StreamT that receives fragments of an unseen possibly infinite semistructured data in the document order through a data stream, and can return the current set of frequent patterns immediately on request at any time. A crucial part of our algorithm is the incremental maintenance of the occurrences of possibly frequent patterns using a tree sweeping technique. We give modifications of the algorithm to other online mining model. We present theoretical and empirical analyses to evaluate the performance of the algorithm.
Tatsuya Asai, Hiroki Arimura, Kenji Abe, Shinji Ka
Added 14 Jul 2010
Updated 14 Jul 2010
Type Conference
Year 2002
Where ICDM
Authors Tatsuya Asai, Hiroki Arimura, Kenji Abe, Shinji Kawasoe, Setsuo Arikawa
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