Ensemble Clustering for Novelty Detection in Data Streams

  • Kemilly Dearo GarciaEmail author
  • Elaine Ribeiro de Faria
  • Cláudio Rebelo de Sá
  • João Mendes-Moreira
  • Charu C. Aggarwal
  • André C. P. L. F. de Carvalho
  • Joost N. Kok
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11828)


In data streams new classes can appear over time due to changes in the data statistical distribution. Consequently, models can become outdated, which requires the use of incremental learning algorithms capable of detecting and learning the changes over time. However, when a single classification model is used for novelty detection, there is a risk that its bias may not be suitable for new data distributions. A solution could be the combination of several models into an ensemble. Besides, because models can only be updated when labeled data arrives, we propose two unsupervised ensemble approaches: one combining clustering partitions using the same clustering technique; and other using different clustering techniques. We compare the performance of the proposed methods with well known novelty detection algorithms. The methods were tested on datasets commonly used in the novelty detection literature. The experimental results show that proposed ensembles have competitive performance for novelty detection in data streams.


Novelty detection Ensembles Clustering Data streams 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Kemilly Dearo Garcia
    • 1
    • 2
    Email author
  • Elaine Ribeiro de Faria
    • 3
  • Cláudio Rebelo de Sá
    • 1
  • João Mendes-Moreira
    • 4
  • Charu C. Aggarwal
    • 5
  • André C. P. L. F. de Carvalho
    • 2
  • Joost N. Kok
    • 1
  1. 1.University of TwenteEnschedeThe Netherlands
  2. 2.University of São PauloSão PauloBrazil
  3. 3.Fed. University of UberlandiaUberlandiaBrazil
  4. 4.LIAAD-INESC TEC, Faculty of EngineeringUniversity of PortoPortoPortugal
  5. 5.IBM T.J. Watson Research CenterYorktownUSA

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