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Subspace data stream clustering with global and local weighting models

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Abstract

Subspace clustering discovers clusters embedded in multiple, overlapping subspaces of high dimensional data. It has been successfully applied in many domains. Data streams are ordered and potentially infinite sequences of data points created by a typically non-stationary data generating process. Clustering this type of data requires some restrictions in time and memory. In this paper, we propose the S2G-Stream algorithm based on growing neural gas and soft subspace clustering. We introduce two types of entropy weighting for both features and blocks, and also two weighting models (local and global). Experiments on public datasets demonstrated the ability of S2G-Stream to detect relevant features and blocks and to provide the best partitioning of the data.

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Notes

  1. https://github.com/Clustering4Ever/Clustering4Ever.

  2. http://impca.curtin.edu.au/local/software/synthetic-data-sets.tar.bz2.

  3. https://github.com/mhahsler/streamMOA.

  4. https://github.com/georgekatona/Clique.

  5. https://github.com/OguzhanOktay-Buyuk/PROCLUS-Python3.

  6. https://github.com/Yanis2016/Weighted-K-Means-clustering.

  7. Percentage of time with abnormal short-term variability.

  8. Mean value of short-term variability.

  9. Percentage of time with abnormal long-term variability.

  10. Mean value of long-term variability.

  11. https://github.com/Clustering4Ever/Clustering4Ever.

  12. https://github.com/Spark-clustering-notebook/Clustering4Ever-Notebooks/tree/master/SparkNotebooks/0.9.4.

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Acknowledgements

The works of Mr. Ghesmoune Mohamed inspired this paper. We address him with a special thanks for his availability and the time he gave to this work.

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Authors and Affiliations

  1. University Sorbonne Paris Nord, LIPN-UMR 7030 99, av. J-B Clément, 93430, Villetaneuse, France

    Mohammed Oualid Attaoui, Hanene Azzag & Mustapha Lebbah

  2. LabRI Laboratory, Higher School of Computer Science (ESI-SBA), Sidi Bel Abbès, Algeria

    Mohammed Oualid Attaoui & Nabil Keskes

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  1. Mohammed Oualid Attaoui
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  2. Hanene Azzag
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Correspondence to Mohammed Oualid Attaoui.

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Attaoui, M.O., Azzag, H., Lebbah, M. et al. Subspace data stream clustering with global and local weighting models. Neural Comput & Applic 33, 3691–3712 (2021). https://doi.org/10.1007/s00521-020-05184-z

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