Research

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 four PhD students. They work on problems related to general data clustering, and integration of semi-supervised and unsupervised learning applied to malware classification. If you are interested in pursuing a PhD in machine learning do feel free to contact me.

PhD Scholarships are always advertised in www.KDnuggets.com.

Publications

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.
[pdf][doi]

 


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.
[pdf][Matlab][doi]

 


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

 


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.
[pdf][Matlab][doi]

 

 


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.
[pdf][Matlab][doi]

 

 


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.
[pdf][Matlab][doi]

 

 


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.
[pdf][Matlab][info]

 

 


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.
[pdf][doi]

 

 


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.
[pdf][Matlab][doi]

 

 


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.
[pdf][info]

 

 


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.
[pdf][doi]

 

 


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.
[pdf][Matlab][info]

 

 


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.
[pdf][Matlab][info]

 

 


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.
[pdf][doi]

 

 


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.
[pdf][info]

 

 


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.
[pdf][info]

 

 


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.
[pdf][doi]

 

 


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.
[pdf][doi]

 

 


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.
[pdf][Matlab][doi]

 

 


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.
[pdf][doi]

 

 


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.
[pdf]

 

 


Amorim, R.C. and Komisarczuk P., On Partitional Clustering of Malware, CyberPatterns 2012, Oxford Brookes, Oxford, 9-10 July 2012.
[pdf][info] [blog post]

 

 


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.
[pdf][Matlab][Matlab][doi]

 

 


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

 

 


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

 

 


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.
[info]

 

 


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.
[pdf][info]

 

 


Amorim, R. C., Computational Methods of Feature Selection \96 Book Review, Information Processing & Management, Elsevier, 2009.
[doi]

 

 


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.
[pdf][doi]

 

 


Amorim, R. C., Matrix Methods in Data Mining and Pattern Recognition \96 Book Review, Cognitive Systems Research, Elsevier, 2009.
[doi]

 

 


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.
[pdf][doi]

 

 


Amorim, R. C., Successes and New Directions in Data Mining \96 Book Review, Information Retrieval, Springer, 2008.
[doi]

 

 


 

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