Dr. Renato Cordeiro de Amorim

Senior Lecturer in Computer Science and AI
School of Computer Science and EE
University of Essex
Colchester CO4 3SQ

r dot amorim at essex dot ac dot uk

Short bio

I am originally Brazilian (well, as much as possible... for someone who doesn't know anything about football), and I moved to the UK in 2003 (the original plan was to stay in the UK for a year... that was over 15 years ago).

I have a mix of academic and commercial experience. Currently, I am a Senior Lecturer in Computer Science and AI at the University of Essex. Previously I held positions at the University of Hertfordshire, Birkbeck University of London, and I worked in software development using mainly C++, VB .Net and VBA.


I am particularly interested in feature weighting as well as unsupervised and semi-supervised learning. I have published various papers with applications in fields such as security, biosignal processing (EEG) and data mining.

I currently supervise two PhD projects. They are related to unsupervised feature selection, and feature weighting in density-based clustering algorithms. If you are interested in pursuing a PhD in data science do feel free to contact me. PhD studenships are usually advertised at KDnuggets.

Grants and awards

  • 2019-2022 Develop AI methods to optimise interactions with customers.
    BT and Innovate UK. GBP 247,874 (PI).

  • 2018-2021 Artificial Intelligence Triage System for the MSK Service.
    Provide CIC and Innovate UK. GBP 578,252 (CO-PI).

  • 2018-2020 Anomaly detection for fraud prevention within the Brazilian Governmental Public Key Infrastructure.
    Royal Society, GBP 63,130 (CO-PI).

  • 2017-2018 Effortless Accountability.
    ESRC IAA Challenge lab: Essex County Council. GBP 10,000 (CO-I).

  • 2017 Chikio Hayashi Award.
    International Federation of Classification Societies (IFCS), USD$ 1,000.

  • 2017-2018 Beyond clustering with a single distance.
    Microsoft Azure Research Award. USD$ 5,000.00 (PI).

  • 2013/14 Excellence in Teaching Award.
    Glyndwr University (nominated).

Editorial work
I am an Associate Editor for the following journals:

Selected works
Internet profiles: Google scholar, ResearchGate.

  • Harris, D., Amorim, R.C., An extensive empirical comparison of k-means initialisation algorithms, IEEE Access, IEEE, vol. 10, pp. 58752-58768, 2022.

  • Alamos, A.J.B., Hashempour, R., Rumble, D., Jameel, S., Amorim, R.C., Unified Transformer Multi-Task Learning for Intent Classification With Entity Recognition, IEEE Access, IEEE, vol. 9, pp. 147306-147314, 2021.

  • Amorim, R.C., Makarenkov, V., Improving cluster recovery with feature rescaling factors, Applied Intelligence, Springer, vol. 51, pp. 5759–5774, 2021.

  • Amorim, R.C., Ruiz, C.D.L., Identifying meaningful clusters in malware data, Expert Systems with Applications, Elsevier, vol. 177, 2021.

  • Amorim, R.C., Makarenkov, V., Mirkin, B., Core clustering as a tool for tackling noise in cluster labels. Journal of Classification, Springer, vol. 37, pp. 143–157, 2020

  • Amorim, R.C., Unsupervised feature selection for large data sets. Pattern Recognition Letters, Elsevier, vol. 128, pp. 183-189, 2019.

  • Chowdhury, S., Amorim, R.C., An efficient density-based clustering algorithm using reverse nearest neighbour. Proceedings of the Computing Conference, 2019, London, UK.

  • Panday, D., Amorim, R.C., Lane, P., Feature weighting as a tool for unsupervised feature selection. Information Processing Letters, Elsevier, vol. 129, pp. 44-52, 2018.

  • Amorim, R.C., Tahiri, N., Mirkin, B., Makarenkov, V., A Median-Based Consensus Rule for Distance Exponent Selection in the Framework of Intelligent and Weighted Minkowski Clustering, In: Palumbo F., Montanari A., Vichi M. (eds) Data Science. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, pp. 97-110, 2017.

  • Amorim, R.C., Shestakov, A., Makarenkov, V., Mirkin, B., The Minkowski central partition as a pointer to a suitable distance exponent and consensus partitioning, Pattern Recognition, Elsevier, vol. 67, pp. 62-72, 2017.

  • Amorim, R.C., Makarenkov, V., Mirkin, B., A-Ward: Effective hierarchical clustering using the Minkowski metric and a fast k-means initialisation, Information Sciences, Elsevier, Vol. 370-371, pp. 343-354, 2016.

  • Amorim, R.C., A survey on feature weighting based K-Means algorithms, Journal of Classification, Springer, 33(2), pp. 210-242, 2016.

  • Amorim, R.C., Makarenkov, V., Applying subclustering and Lp distance in Weighted K-Means with distributed centroids, Neurocomputing, Elsevier, Vol. 173(3), pp.700-707, 2016.

  • Amorim, R.C., Hennig, C., Recovering the number of clusters in data sets with noise features using feature rescaling factors, Information Sciences, Elsevier, vol. 324, pp. 126-145, 2015.

  • Amorim, R.C., Feature relevance in Ward's hierarchical clustering using the Lp norm, Journal of Classification, Springer, vol. 32(1), pp. 46-62, 2015.

  • Amorim, R.C., and Mirkin B., A clustering based approach to reduce feature redundancy. In: Skulimowski, A.M.J., Kacprzyk, J. (Eds). Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions. Advances in Intelligent Systems and Computing. Springer.

  • Puttaroo, M., Komisarczuk, P., Amorim, R.C., Challenges in developing Capture-HPC exclusion lists, Proceedings of the 7th International Conference on Security of Information and Networks, 2014, Glasgow, UK.

  • Zampieri, M., and Amorim, R.C., Between Sound and Spelling: Combining Phonetics and Clustering Algorithms to Improve Target Word Recovery. Proceedings of the 9th International Conference on Natural Language Processing, 2014, Warsaw, Poland.

  • Amorim, R.C. and Komisarczuk, P., Towards effective malware clustering: reducing false negatives through feature weighting and the Lp metric. In: Issac, B. and Israr, N. (Eds) Case Studies in Secure Computing - Achievements and Trends. CRC Press, 2014.

  • Amorim, R.C. and Komisarczuk, P., Partitional Clustering of Malware using K-Means. In: Blackwell, C. and Zhu, H. (Eds) Cyberpatterns: Unifying Design Patterns with Security, Attack and Forensic Patterns. Springer, pp. 223-233, 2014.

  • Amorim, R.C. and Mirkin, B., Removing redundant features via clustering: preliminary results in mental task separation. Proceedings of the 8th International Conference on Knowledge, Information and Creativity Support Systems, 7-9 November 2013, Krakow, Poland.

  • Amorim, R.C. and Zampieri, M., Effective Spell Checking Methods Using Clustering Algorithms. Recent Advances in Natural Language Processing, 7-13 September 2013, Hissar, Bulgaria.

  • Amorim, R.C. and Mirkin, B., Selecting the Minkowski exponent for intelligent K-Means with feature weighting. In: Pardalos, P., Goldengorin, B., Aleskerov, F. (Eds), Clusters, orders, trees: methods and applications, Springer, 2013.

  • Puttaaroo, M., Komisarczuk, P., Amorim, R.C., On Drive-by-Download Attacks and Malware Classification. Fifth International Conference on Internet Technologies & Applications (ITA), Wrexham, Wales, 10 to 13 September 2013.

  • Austin, A., Amorim, R.C., Griffin, A., Targeted tutorials and the use of ASSIST to support student learning. Education, Learning, Styles, Individual differences Network (ELSIN), Billund, Denmark, 18 to 20 June 2013.

  • Amorim, R.C., An Empirical Evaluation of Different Initializations on the Number of K-means Iterations. MICAI - Lecture Notes in Computer Sciences, 7629, pp. 15-26, 2013.

  • Amorim, R.C., Constrained Clustering with Minkowski Weighted K-Means. 13th IEEE International Symposium on Computational Intelligence and Informatics, pp. 14-17, Budapest, Hungary, 20-22 November 2012.

  • Amorim R.C. and Fenner, T., Weighting Features for Partition Around Medoids using the Minkowski Metric. IDA - Lecture Notes in Computer Science, 7619, pp. 35-44, 2012.

  • Amorim, R.C. and Komisarczuk P., On Initializations for the Minkowski Weighted K-Means. IDA - Lecture Notes in Computer Science, 7619, pp.45-55, 2012.

  • Amorim, R.C., Mirkin B., Gan J.Q., Anomalous Pattern based Clustering of Mental Tasks with Subject Independent Learning: Some Preliminary Results, Artificial Intelligence Research, 1(1), pp. 46-54, 2012.

  • Amorim, R.C. and Komisarczuk P., On Partitional Clustering of Malware, CyberPatterns 2012, Oxford Brookes, Oxford, 9-10 July 2012.

  • Amorim, R.C. and Mirkin, B., Minkowski Metric, Feature Weighting and Anomalous Cluster Initialisation in K-Means Clustering, Pattern Recognition, vol. 45(3), pp. 1061-1075, 2012.

  • Amorim, R.C. and Komisarczuk, P., On the Future of Capture-HPC: A Malware Survey, Technical Report 01/2012, University of West London, 2012.

  • Amorim, R.C. Feature Weighting for Clustering Using K-Means and the Minkowski Metric, Lambert Academic Publishing, 2012.

  • Amorim, R.C. and Mirkin, B., Minkowski Metric for Feature Weighting, Proceedings of the International Classification Conference, University of St. Andrews, Scotland, 11-15 July, 2011.

  • Amorim, R. C., Mirkin, B. and Gan, J. Q., A method for classifying mental tasks in the space of EEG transforms, UKCI, University of Nottingham, 7-9 September, 2009.

  • Amorim, R. C., Computational Methods of Feature Selection - Book Review, Information Processing & Management, Elsevier, 2009.

  • Amorim, R. C., An Adaptive Spell Checker Based on PS3M: Improving the Clusters of Replacement Words, The Sixth International Conference on Computer Recognition Systems, Advances in Intelligent and Soft Computing, Springer-Verlag, 2009.

  • Amorim, R. C., Matrix Methods in Data Mining and Pattern Recognition, Book Review, Cognitive Systems Research, Elsevier, 2009.

  • Amorim, R. C., Constrained Intelligent K-Means: Improving Results with Limited Previous Knowledge, The 2nd International Conference on Advanced Engineering Computing and Applications in Sciences, IEEE Computer Society Press, Spain, 2008.

  • Amorim, R. C., Successes and New Directions in Data Mining - Book Review, Information Retrieval, Springer, 2008.