Adaptive XML Tree Classification on Evolving Data Streams

  • Albert Bifet
  • Ricard Gavaldà
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5781)


We propose 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. We use them to reduce the number of classification features. We present a new framework for XML tree stream classification. For the first component of our classification framework, we use closed tree mining algorithms for evolving data streams. For the second component, we use state of the art classification methods for data streams. To the best of our knowledge this is the first work on tree classification in streaming data varying with time. We give a first experimental evaluation of the proposed classification method.


Frequent Pattern Concept Drift Frequent Tree Closed Pattern Maximal Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Albert Bifet
    • 1
  • Ricard Gavaldà
    • 1
  1. 1.Universitat Politècnica de CatalunyaBarcelonaSpain

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