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Adaptive XML Tree Classification on Evolving Data Streams

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Executive Summary

This paper proposes a new method to classify patterns, using closed and maximal frequent patterns as features. Generally, classification requires a previous mapping from the patterns to classify to vectors of features, and frequent patterns have been used as features in the past. Closed patterns maintain the same information as frequent patterns using less space and maximal patterns maintain approximate information. The authors use them to reduce the number of classification features. This paper presents a new framework for XML tree stream classification. For the first component of the classification framework, the authors use closed tree mining algorithms for evolving data streams.

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