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Drifted Data Stream Clustering Based on ClusTree Algorithm

  • Jakub ZgrajaEmail author
  • Michał Woźniak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10870)

Abstract

Correct recognition of the possible changes in data streams, called concept drifts plays a crucial role in constructing the appropriate model learning strategy. This paper focuses on the unsupervised learning model for non-stationary data streams, where two significant modifications of the ClustTree algorithm are presented. They allow the clustering model to be adapted to the changes caused by a concept drift. An experimental study conducted on a set of benchmark data streams proves the usefulness of the proposed solutions.

Keywords

Concept drift Data streams ClusTree On-line clustering 

Notes

Acknowledgments

This work was supported by Statutory Fund of the Department of Systems and—Computer Networks, Faculty of Electronics, Wroclaw University of Science and Technology.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Electronics, Department of Systems and Computer NetworksWroclaw University of Science and TechnologyWrocławPoland

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