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Research

Dr Nicos Pavlidis

Senior Lecturer

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  1. 2020
  2. Published

    Nonlinear Dimensionality Reduction for Clustering

    Tasoulis, S., Pavlidis, N. & Roos, T., 1/11/2020, In: Pattern Recognition. 107, 11 p., 107508.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

  3. Published

    Subspace Clustering with Active Learning

    Peng, H. & Pavlidis, N., 24/02/2020, Proceedings of 2019 IEEE International Conference on Big Data. IEEE, 10 p. 9006361

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

  4. Published

    Robust Functional Regression for Outlier Detection

    Hullait, H., Leslie, D. S., Pavlidis, N. G. & King, S., 23/01/2020, Advanced Analytics and Learning on Temporal Data: 4th ECML PKDD Workshop, AALTD 2019, Würzburg, Germany, September 20, 2019, Revised Selected Papers. Lemaire, V., Malinowski, S., Bagnall, A., Bondu, A., Guyet, T. & Tavenard, R. (eds.). Cham: Springer, p. 3-13 11 p. (Lecture Notes in Computer Science; vol. 11986).

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

  5. 2019
  6. Published

    PPCI: an R Package for Cluster Identification using Projection Pursuit

    Hofmeyr, D. & Pavlidis, N., 27/12/2019, In: The R Journal.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

  7. Published

    Minimum spectral connectivity projection pursuit: Divisive clustering using optimal projections for spectral clustering

    Hofmeyr, D., Pavlidis, N. G. & Eckley, I. A., 1/03/2019, In: Statistics and Computing. 29, 2, p. 391–414 24 p.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

  8. Published

    Subspace Clustering of Very Sparse High-Dimensional Data

    Peng, H., Pavlidis, N. G., Eckley, I. A. & Tsalamanis, I., 24/01/2019, 2018 IEEE International Conference on Big Data (Big Data). IEEE, p. 3780-3783 4 p.

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

  9. 2017
  10. Published

    Minimum density hyperplanes in the feature space

    Yates, K. & Pavlidis, N. G., 11/02/2017, Big Data (Big Data), 2016 IEEE International Conference on. IEEE, p. 3613-3618 6 p.

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

  11. 2016
  12. Published

    Minimum Density Hyperplanes

    Pavlidis, N. G., Hofmeyr, D. & Tasoulis, S., 28/09/2016, In: Journal of Machine Learning Research. 17, 156, p. 1-33 33 p.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

  13. Published

    Divisive clustering of high dimensional data streams

    Hofmeyr, D., Pavlidis, N. & Eckley, I., 09/2016, In: Statistics and Computing. 26, 5, p. 1101–1120 20 p.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

  14. 2015
  15. Published

    Maximum Clusterability Divisive Clustering

    Hofmeyr, D. & Pavlidis, N. G., 7/12/2015, Computational Intelligence, 2015 IEEE Symposium Series on . Cape Town: IEEE, p. 780-786 7 p.

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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