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Stream Data Mining Using the MOA Framework

  • Philipp Kranen
  • Hardy Kremer
  • Timm Jansen
  • Thomas Seidl
  • Albert Bifet
  • Geoff Holmes
  • Bernhard Pfahringer
  • Jesse Read
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7239)

Abstract

Massive Online Analysis (MOA) is a software framework that provides algorithms and evaluation methods for mining tasks on evolving data streams. In addition to supervised and unsupervised learning, MOA has recently been extended to support multi-label classification and graph mining. In this demonstrator we describe the main features of MOA and present the newly added methods for outlier detection on streaming data. Algorithms can be compared to established baseline methods such as LOF and ABOD using standard ranking measures including Spearman rank coefficient and the AUC measure. MOA is an open source project and videos as well as tutorials are publicly available on the MOA homepage.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Philipp Kranen
    • 1
  • Hardy Kremer
    • 1
  • Timm Jansen
    • 1
  • Thomas Seidl
    • 1
  • Albert Bifet
    • 2
  • Geoff Holmes
    • 2
  • Bernhard Pfahringer
    • 2
  • Jesse Read
    • 2
  1. 1.Data Management and Exploration GroupRWTH Aachen UniversityGermany
  2. 2.Department of Computer ScienceUniversity of WaikatoHamiltonNew Zealand

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