Dr Renato Amorim
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Email
r.amorim@essex.ac.uk -
Location
3A.521, Colchester Campus
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Academic support hours
Tuesdays: 15:00 to 16:00 Thursdays: 15:00 to 16:00
Profile
Qualifications
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PhD Birkbeck, University of London, (2011)
Research and professional activities
Research interests
Automatic feature weighting in clustering of large data sets
Unsupervised feature selection
Machine Learning
Exploratory data analysis
Teaching and supervision
Current teaching responsibilities
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Team Project Challenge (CE101)
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Introduction to Programming in Python (CE705)
Previous supervision
Degree subject: Computer Science
Degree type: Master of Science (by Dissertation)
Awarded date: 26/4/2021
Publications
Publications (1)
Amorim, RCD., (2023). On large sum-free sets: revised bounds and patterns
Journal articles (21)
Rykov, A., De Amorim, RC., Makarenkov, V. and Mirkin, B., (2024). Inertia-Based Indices to Determine the Number of Clusters in K-Means: An Experimental Evaluation. IEEE Access. 12, 11761-11773
Chowdhury, S., Helian, N. and Amorim, R., (2023). Feature weighting in DBSCAN using reverse nearest neighbours. Pattern Recognition. 137, 109314-109314
Amorim, R., (2023). On Sum-Free Subsets of Abelian Groups. Axioms. 12 (8), 724-724
Amorim, R. and Makarenkov, V., (2023). On k-means iterations and Gaussian clusters. Neurocomputing. 553, 126547-126547
Harris, S. and Cordeiro De Amorim, R., (2022). An extensive empirical comparison of k-means initialisation algorithms. IEEE Access. 10, 58752-58768
Amorim, R. and Makarenkov, V., (2021). Improving cluster recovery with feature rescaling factors. Applied Intelligence. 51 (8), 5759-5774
Benayas, A., Hashempour, R., Rumble, D., Jameel, S. and De Amorim, RC., (2021). Unified Transformer Multi-Task Learning for Intent Classification With Entity Recognition. IEEE Access. 9, 147306-147314
Amorim, R. and Lopez Ruiz, CD., (2021). Identifying meaningful clusters in malware data. Expert Systems with Applications. 177, 114971-114971
Cordeiro de Amorim, R., Makarenkov, V. and Mirkin, B., (2020). Core clustering as a tool for tackling noise in cluster labels. Journal of Classification. 37 (1), 143-157
Cordeiro de Amorim, R., (2019). Unsupervised feature selection for large data sets. Pattern Recognition Letters. 128, 183-189
Panday, D., Amorim, RC. and Lane, P., (2018). Feature weighting as a tool for unsupervised feature selection. Information Processing Letters. 129, 44-52
Cordeiro de Amorim, R., Shestakov, A., Mirkin, B. and Makarenkov, V., (2017). The Minkowski central partition as a pointer to a suitable distance exponent and consensus partitioning. Pattern Recognition. 67, 62-72
Cordeiro de Amorim, R., Makarenkov, V. and Mirkin, B., (2016). A-Wardpβ: Effective hierarchical clustering using the Minkowski metric and a fast k-means initialisation. Information Sciences. 370-371, 343-354
Amorim, RC. and Makarenkov, V., (2016). Applying subclustering and Lp distance in Weighted K-Means with distributed centroids. Neurocomputing. 173 (P3), 700-707
Amorim, RC., (2016). A survey on feature weighting based K-Means algorithms. Journal of Classification. 33 (2), 210-242
Amorim, RC. and Hennig, C., (2015). Recovering the number of clusters in data sets with noise features using feature rescaling factors. Information Sciences. 324, 126-145
Amorim, RC., (2015). Feature Relevance in Ward's Hierarchical Clustering Using the L (p) Norm. Journal of Classification. 32 (1), 46-62
Cordeiro de Amorim, R. and Mirkin, B., (2012). Minkowski metric, feature weighting and anomalous cluster initializing in K-Means clustering. Pattern Recognition. 45 (3), 1061-1075
Amorim, R., Mirkin, B. and Gan, JQ., (2012). Anomalous pattern based clustering of mental tasks with subject independent learning – some preliminary results. Artificial Intelligence Research. 1 (1), 55-55
de Amorim, RC., (2009). . Information Processing & Management. 45 (4), 490-493
Wermter, S. and de Amorim, RC., (2009). . Cognitive Systems Research. 10 (4), 377-379
Book chapters (8)
de Amorim, RC., Tahiri, N., Mirkin, B. and Makarenkov, V., (2017). A Median-Based Consensus Rule for Distance Exponent Selection in the Framework of Intelligent and Weighted Minkowski Clustering. In: Data Science. Springer, Cham. 97- 110. 9783319557229
Cordeiro De Amorim, R. and Mirkin, B., (2016). A clustering based approach to reduce feature redundancy. Springer
de Amorim, RC. and Mirkin, B., (2015). A clustering based approach to reduce feature redundancy. In: Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions. Springer. 465- 475. 9783319190891
de Amorim, RC. and Komisarczuk, P., (2014). Partitional Clustering of Malware using K-Means. In: Cyberpatterns: Unifying Design Patterns with Security and Attack Patterns. Springer. 223- 233. 331904446X. 9783319044460
de Amorim, RC. and Komisarczuk, P., (2014). Towards effective malware clustering: reducing false negatives through feature weighting and the Lp metric. In: Case Studies in Secure Computing - Achievements and Trends. CRC Press. 295- 310. 9781482207064
Cordeiro De Amorim, R. and Mirkin, B., (2014). Selecting the Minkowski Exponent for Intelligent K-Means with Feature Weighting. Springer
Cordeiro De Amorim, R. and Komisarczuk, P., (2014). Towards effective malware clustering: reducing false negatives through feature weighting and the Lp metric. CRC Press
de Amorim, RC. and Mirkin, B., (2013). Selecting the Minkowski exponent for intelligent K-Means with feature weighting. In: Clusters, orders, trees: methods and applications. Springer. 103- 117. 9781493907410
Conferences (17)
Zampieri, M. and de Amorim, RC., (2014). Between Sound and Spelling: Combining Phonetics and Clustering Algorithms to Improve Target Word Recovery
Puttaroo, M., Komisarczuk, P. and de Amorim, RC., (2014). Challenges in developing Capture-HPC exclusion lists
de Amorim, RC., (2013). An Empirical Evaluation of Different Initializations on the Number of K-means Iterations
Austing, A., de Amorim, RC. and Griffin, A., (2013). Targeted tutorials and the use of ASSIST to support student learning
Puttaroo, M., Komisarczuk, P. and de Amorim, RC., (2013). ON DRIVE-BY-DOWNLOAD ATTACKS AND MALWARE CLASSIFICATION
de Amorim, RC. and Zampieri, M., (2013). Effective Spell Checking Methods Using Clustering Algorithms
de Amorim, RC. and Mirkin, B., (2013). Removing redundant features via clustering: preliminary results in mental task separation
Cordeiro De Amorim, R., (2013). An Empirical Evaluation of Different Initializations on the Number of K-Means Iterations
Austin, A., Cordeiro de Amorim, R. and Griffin, A., (2013). Providing an enhanced tutorial system to support student learning Society for Research
Amorim, RC., (2013). Constrained Clustering with Minkowski Weighted K-Means
de Amorim, RC. and Komisarczuk, P., (2012). On partitional clustering of malware
de Amorim, RC. and Komisarczuk, P., (2012). On Initializations for the Minkowski Weighted K-Means
de Amorim, RC. and Fenner, T., (2012). Weighting features for Partition Around Medoids using the Minkowski metric
de Amorim, RC., (2009). An adaptive spell checker based on PS3M: Improving the clusters of replacement words
Amorim, R., Mirkin, B. and Gan, JQ., (2009). A method for classifying mental tasks in the space of EEG transforms
Cordeiro De Amorim, R., Mirkin, B. and Q Gan, J., (2009). A method for classifying mental tasks in the space of EEG transforms
Amorim, R., (2008). Constrained Intelligent K-Means: Improving Results with Limited Previous Knowledge.
Reports and Papers (2)
Chowdhury, S. and de Amorim, RC., (2019). An Efficient Density-Based Clustering Algorithm Using Reverse Nearest Neighbour
de Amorim, RC. and Komisarczuk, P., (2012). On the Future of Capture-HPC: A Malware Survey
Thesis dissertation (1)
de Amorim, RC., (2011). Learning feature weights for K-Means clustering using the Minkowski metric. PhD Thesis
Other (1)
de Amorim, RC., (2012).Feature Weighting for Clustering: Using K-Means and the Minkowski Metric,LAP LAMBERT Academic Publishing
Grants and funding
2023
To design and incorporate machine learning capabilities and robotic process automation tools into the administrative workflow of a traditionally-structured manufacturing and supply organisation.
Innovate UK (formerly Technology Strategy Board)
2018
Provide KTP 2018
Provide
Develop AI methods to optimise interactions with customers.
Innovate UK (formerly Technology Strategy Board)
Anomaly detection for fraud prevention within the Brazilian Governmental Public Key Infrastructure
The Royal Society
Provide KTP 2018
Innovate UK (formerly Technology Strategy Board)
Contact
Academic support hours:
Tuesdays: 15:00 to 16:00 Thursdays: 15:00 to 16:00
