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Online Algorithms for Mining Semi-structured Data Stream


Tatsuya Asai, Hiroki Arimura, Kenji Abe, Shinji Kawasoe, and Setsuo Arikawa

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Abstract

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 semi-structured 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 on-line mining model. We present theoretical and empirical analyses to evaluate the performance of the algorithm.


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