Ensemble Clustering for Novelty Detection in Data Streams

Kemilly Dearo Garcia, Elaine Ribeiro de Faria, Cláudio Rebelo de Sá, João Mendes-Moreira, Charu C. Aggarwal, André C.P.L.F. de Carvalho, Joost N. Kok

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)


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 Consequently, models can become outdated, which requires the use of incremental learning algorithms capable of detecting and learning the changes over time.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.

Original languageEnglish
Title of host publicationDiscovery Science 22nd International Conference, DS 2019, Proceedings
EditorsPetra Kralj Novak, Sašo Džeroski, Tomislav Šmuc
Number of pages11
ISBN (Print)9783030337773
Publication statusPublished - 2019
Externally publishedYes
Event22nd International Conference on Discovery Science, DS 2019 - Split, Croatia
Duration: 28 Oct 201930 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11828 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Conference on Discovery Science, DS 2019

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2019.


  • Clustering
  • Data streams
  • Ensembles
  • Novelty detection


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