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Results for clustering

Publications & Outputs

  1. Bayesian non-parametric models for zoonotic disease applications

    Miller, P., 12/2020, Lancaster University. 201 p.

    Research output: Thesis › Doctoral Thesis

  2. Strong spatial embedding of social networks generates nonstandard epidemic dynamics independent of degree distribution and clustering

    Haw, D. J., Pung, R., Read, J. M. & Riley, S., 22/09/2020, In: Proceedings of the National Academy of Sciences of the United States of America. 117, 38, p. 23636-23642 7 p.

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

  3. A Distance-Type-Insensitive Clustering Approach

    Gu, X., Angelov, P. P. & Zhao, Z., 1/04/2019, In: Applied Soft Computing. 77, p. 622-634 13 p.

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

  4. A new type of distance metric and its use for clustering

    Gu, X., Angelov, P. P., Kangin, D. & Principe, J., 09/2017, In: Evolving Systems. 8, 3, p. 167-177 11 p.

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

  5. Real-time Recognition of Calling Pattern and Behaviour of Mobile Phone users through Anomaly Detection and Dynamically Evolving Clustering

    Iglesias, J., Ledezma, A., Sanchis, A. & Angelov, P. P., 5/08/2017, In: Applied Sciences. 7, 8, p. 1-14 14 p., 798.

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

  6. 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

  7. Consistent algorithms for clustering time series

    Khaleghi, A., Ryabko, D., Mari, J. & Preux, P., 03/2016, In: Journal of Machine Learning Research. 17, 3, p. 1-32 32 p.

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

  8. A nested hierarchy of dynamically evolving clouds for big data structuring and searching

    Angelov, P. & Sadeghi Tehran, P., 8/08/2015, In: Procedia Computer Science. 53, p. 1-8 8 p.

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

  9. A comparative study of autonomous learning outlier detection methods applied to fault detection

    Bezerra, C. G., Costa, B. S. J., Guedes, L. A. & Angelov, P. P., 08/2015, Proceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) . IEEE, p. 1-7 7 p.

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

  10. A new online clustering approach for data in arbitrary shaped clusters

    Hyde, R. & Angelov, P., 24/06/2015, Cybernetics (CYBCONF), 2015 IEEE 2nd International Conference on . IEEE, p. 228-233 6 p. 78

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

  11. Data density based clustering

    Hyde, R. & Angelov, P., 8/09/2014, Computational Intelligence (UKCI), 2014 14th UK Workshop on. IEEE, p. 1-7 7 p.

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

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