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

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  • Phillip Kranen, Data Management and Data Exploration Group, RWTH Aachen University, Germany
  • Ira Assent
  • Thomas Seidl, Data Management and Data Exploration Group, RWTH Aachen University, Germany
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.
Original languageEnglish
JournalDatenbank-Spektrum
Volume12
Issue1
Pages (from-to)43-50
Number of pages8
ISSN1618-2162
DOIs
Publication statusPublished - 2012

    Research areas

  • Data mining, Stream processing, Anytime algorithms

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