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Rykov, Andrei and Cordeiro De Amorim, Renato and Makarenkov, Vladimir and Mirkin, Boris (2024) Inertia-Based Indices to Determine the Number of Clusters in K-Means: An Experimental Evaluation. IEEE Access, 12. pp. 11761-11773. DOI https://doi.org/10.1109/access.2024.3350791

Amorim, Renato and Makarenkov, Vladimir (2023) On k-means iterations and Gaussian clusters. Neurocomputing, 553. p. 126547. DOI https://doi.org/10.1016/j.neucom.2023.126547

Amorim, Renato (2023) On Sum-Free Subsets of Abelian Groups. Axioms, 12 (8). p. 724. DOI https://doi.org/10.3390/axioms12080724

Chowdhury, Stiphen and Helian, Na and Amorim, Renato (2023) Feature weighting in DBSCAN using reverse nearest neighbours. Pattern Recognition, 137. p. 109314. DOI https://doi.org/10.1016/j.patcog.2023.109314

Harris, Simon and Cordeiro De Amorim, Renato (2022) An extensive empirical comparison of k-means initialisation algorithms. IEEE Access, 10. pp. 58752-58768. DOI https://doi.org/10.1109/access.2022.3179803

Amorim, Renato and Lopez Ruiz, Carlos D (2021) Identifying meaningful clusters in malware data. Expert Systems with Applications, 177. DOI https://doi.org/10.1016/j.eswa.2021.114971

Amorim, Renato and Makarenkov, Vladimir (2021) Improving cluster recovery with feature rescaling factors. Applied Intelligence, 51 (8). pp. 5759-5774. DOI https://doi.org/10.1007/s10489-020-02108-1

Cordeiro de Amorim, Renato and Makarenkov, Vladimir and Mirkin, Boris (2020) Core clustering as a tool for tackling noise in cluster labels. Journal of Classification, 37 (1). pp. 143-157. DOI https://doi.org/10.1007/s00357-019-9303-4

Cordeiro de Amorim, Renato (2019) Unsupervised feature selection for large data sets. Pattern Recognition Letters, 128. pp. 183-189. DOI https://doi.org/10.1016/j.patrec.2019.08.017

Panday, D and Amorim, RC and Lane, P (2018) Feature weighting as a tool for unsupervised feature selection. Information Processing Letters, 129. pp. 44-52. DOI https://doi.org/10.1016/j.ipl.2017.09.005

Cordeiro de Amorim, R and Shestakov, A and Mirkin, B and Makarenkov, V (2017) The Minkowski central partition as a pointer to a suitable distance exponent and consensus partitioning. Pattern Recognition, 67. pp. 62-72. DOI https://doi.org/10.1016/j.patcog.2017.02.001

Cordeiro de Amorim, R and Makarenkov, V and Mirkin, B (2016) A-Wardpβ: Effective hierarchical clustering using the Minkowski metric and a fast k-means initialisation. Information Sciences, 370-37. pp. 343-354. DOI https://doi.org/10.1016/j.ins.2016.07.076

Amorim, RC (2016) A survey on feature weighting based K-Means algorithms. Journal of Classification, 33 (2). pp. 210-242. DOI https://doi.org/10.1007/s00357-016-9208-4

Amorim, RC and Makarenkov, V (2016) Applying subclustering and Lp distance in Weighted K-Means with distributed centroids. Neurocomputing, 173 (P3). pp. 700-707. DOI https://doi.org/10.1016/j.neucom.2015.08.018

Amorim, RC and Hennig, C (2015) Recovering the number of clusters in data sets with noise features using feature rescaling factors. Information Sciences, 324. pp. 126-145. DOI https://doi.org/10.1016/j.ins.2015.06.039

Amorim, RC (2015) Feature Relevance in Ward's Hierarchical Clustering Using the L (p) Norm. Journal of Classification, 32 (1). pp. 46-62. DOI https://doi.org/10.1007/s00357-015-9167-1

Amorim, Renato and Mirkin, Boris and Gan, John Q (2012) Anomalous pattern based clustering of mental tasks with subject independent learning – some preliminary results. Artificial Intelligence Research, 1 (1). p. 55. DOI https://doi.org/10.5430/air.v1n1p55

Conference or Workshop Item

Raza, Haider and Rathee, Dheeraj and Amorim, Renato and Fasli, Maria (2024) Optimizing Patient Care Pathways: Impact Analysis of an AI-Assisted Smart Referral System for Musculoskeletal Services. In: IEEE International Conference on Digital Health (ICDH), 2024-07-07 - 2024-07-13, Shenzhen, China.

Amorim, RC (2013) Constrained Clustering with Minkowski Weighted K-Means. In: 13th IEEE International Symposium on Computational Intelligence and Informatics (CINTI), 2012, 2012-11-20 - 2012-11-22, Budapest.

Amorim, R and Mirkin, B and Gan, JQ (2009) A method for classifying mental tasks in the space of EEG transforms. In: UNSPECIFIED, ? - ?.

Amorim, Renato (2008) Constrained Intelligent K-Means: Improving Results with Limited Previous Knowledge. In: Second International Conference on Advanced Engineering Computing and Applications in Sciences, 2008-09-29 - 2008-10-04, Valencia, Spain.