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Classification of Data Streams by Incremental Semi-supervised Fuzzy Clustering

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Fuzzy Logic and Soft Computing Applications (WILF 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10147))

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Abstract

Data stream mining refers to methods able to mine continuously arriving and evolving data sequences or even large scale static databases. Mining data streams has attracted much attention recently. Many data stream classification methods are supervised, hence they require labeled samples that are more difficult and expensive to obtain than unlabeled ones. This paper proposes an incremental semi-supervised clustering approach for data stream classification. Preliminary experimental results on the benchmark data set KDD-CUP’99 show the effectiveness of the proposed algorithm.

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Notes

  1. 1.

    Any stream can be turned into a chunked stream by simply waiting for enough data points to arrive.

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Author information

Authors and Affiliations

  1. Computer Science Department, Università degli Studi di Bari “A. Moro”, Via E. Orabona 4, 70126, Bari, Italy

    G. Castellano & A. M. Fanelli

Authors
  1. G. Castellano
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  2. A. M. Fanelli
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Corresponding author

Correspondence to G. Castellano .

Editor information

Editors and Affiliations

  1. University of Naples “Parthenope”, Naples, Italy

    Alfredo Petrosino

  2. University of Salerno, Fisciano, (Salerno), Italy

    Vincenzo Loia

  3. University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

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Castellano, G., Fanelli, A.M. (2017). Classification of Data Streams by Incremental Semi-supervised Fuzzy Clustering. In: Petrosino, A., Loia, V., Pedrycz, W. (eds) Fuzzy Logic and Soft Computing Applications. WILF 2016. Lecture Notes in Computer Science(), vol 10147. Springer, Cham. https://doi.org/10.1007/978-3-319-52962-2_16

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  • DOI: https://doi.org/10.1007/978-3-319-52962-2_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52961-5

  • Online ISBN: 978-3-319-52962-2

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