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Discovering frequent tree patterns over data streams
Conference paper

Discovering frequent tree patterns over data streams

Mark Cheng-Enn Hsieh, Yi-Hung Wu and Arbee L. P. Chen
Proceedings of the Sixth SIAM International Conference on Data Mining, Vol.2006, pp.629-633
2006

Abstract

Data Mining Data streams Tree patterns Engineering (all)
Since tree-structured data such as XML files are widely used for data representation and exchange on the Internet, discovering frequent tree patterns over tree-structured data streams becomes an interesting issue. In this paper, we propose an online algorithm to continuously discover the current set of frequent tree patterns from the data stream. A novel and efficient technique is introduced to incrementally generate all candidate tree patterns without duplicates. Moreover, a framework for counting the approximate frequencies of the candidate tree patterns is presented. Combining these techniques, the proposed approach is able to compute frequent tree patterns with guarantees of completeness and accuracy.

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