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An Index-Inspired Algorithm for Anytime Classification on Evolving Data Streams

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Abstract

Due to the ever growing presence of data streams there has been a considerable amount of research on stream data mining over the past years. Anytime algorithms are particularly well suited for stream mining, since they flexibly use all available time on streams of varying data rates, and are also shown to outperform traditional budget approaches on constant streams. In this article we present an index-inspired algorithm for Bayesian anytime classification on evolving data streams and show its performance on benchmark data sets.

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Notes

  1. Similarity search on large data sets can be sped up to some extend by indexes and other appropriate methods.

  2. Refining 26 classes for the letter data set takes less than 26 pages due a root compression technique employed in [22].

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Acknowledgements

This work has been supported by the UMIC Research Centre, RWTH Aachen University, Germany.

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Authors and Affiliations

  1. Data Management and Data Exploration Group, RWTH Aachen University, Aachen, Germany

    Philipp Kranen & Thomas Seidl

  2. Department of Computer Science, Aarhus University, Aarhus, Denmark

    Ira Assent

Authors
  1. Philipp Kranen
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  2. Ira Assent
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  3. Thomas Seidl
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Correspondence to Philipp Kranen.

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Kranen, P., Assent, I. & Seidl, T. An Index-Inspired Algorithm for Anytime Classification on Evolving Data Streams. Datenbank Spektrum 12, 43–50 (2012). https://doi.org/10.1007/s13222-012-0083-9

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  • DOI: https://doi.org/10.1007/s13222-012-0083-9

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