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SE-Stream: Dimension Projection for Evolution-Based Clustering of High Dimensional Data Streams

  • Conference paper
Knowledge and Systems Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 245))

Abstract

Evaluation-based stream clustering method supports the monitoring and detection of the change in clustering structure. E-Stream is an evolution-based stream clustering method that supports different types of clustering structure evolution which are appearance, disappearance, self-evolution,merge and split. However, its runtime increases and its performance drops when face with high dimensional data. High dimensional data leads to more complexity in the clustering methods. In this paper, we present SE-Stream which extends E-Stream in order to support high dimensional data streams. A projected clustering technique to determine specific subset of dimensions for each cluster is proposed. The proposed technique reduces complexity of calculation. Each cluster describes itself by a set of selected dimensions. Experimental results show that SE-Stream gives better cluster quality compared with E-Stream and HP-Stream, a state of the art algorithm for projected clustering of high dimensional data streams. Further, it gives better execution time compared with E-Stream and comparable execution time compared with HP-Stream.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Kasetsart University, Bangkok, Thailand

    Rattanapong Chairukwattana, Thanapat Kangkachit, Thanawin Rakthanmanon & Kitsana Waiyamai

Authors
  1. Rattanapong Chairukwattana
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  2. Thanapat Kangkachit
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  3. Thanawin Rakthanmanon
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  4. Kitsana Waiyamai
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Corresponding author

Correspondence to Rattanapong Chairukwattana .

Editor information

Editors and Affiliations

  1. School of Knowledge Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan

    Van Nam Huynh

  2. UMR CNRS 7253 Heudiasyc, University of Technology of Compiegne, Compiegne Cedex, France

    Thierry Denoeux

  3. Faculty of Information Technology, Hanoi National University of Education, Hanoi, Vietnam

    Dang Hung Tran

  4. Faculty of Information Technology, University of Engineering and Technology, Hanoi, Vietnam

    Anh Cuong Le

  5. Faculty of Information Technology, University of Engineering and Technology, Hanoi, Vietnam

    Son Bao Pham

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© 2014 Springer International Publishing Switzerland

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Chairukwattana, R., Kangkachit, T., Rakthanmanon, T., Waiyamai, K. (2014). SE-Stream: Dimension Projection for Evolution-Based Clustering of High Dimensional Data Streams. In: Huynh, V., Denoeux, T., Tran, D., Le, A., Pham, S. (eds) Knowledge and Systems Engineering. Advances in Intelligent Systems and Computing, vol 245. Springer, Cham. https://doi.org/10.1007/978-3-319-02821-7_32

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  • DOI: https://doi.org/10.1007/978-3-319-02821-7_32

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02820-0

  • Online ISBN: 978-3-319-02821-7

  • eBook Packages: EngineeringEngineering (R0)

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