People

Dr Renato Amorim

Lecturer
School of Computer Science and Electronic Engineering (CSEE)
Dr Renato Amorim
  • Email

  • Telephone

    +44 (0) 1206 872895

  • Location

    4B.522, Colchester Campus

  • Academic support hours

    Tuesdays: 15:00 to 16:00 Thursdays: 14:00 to 15:00

Profile

Qualifications

  • PhD Birkbeck, University of London, (2011)

Research and professional activities

Research interests

Clustering

Unsupervised feature selection

Machine Learning

Teaching and supervision

Current teaching responsibilities

  • Databases and Information Retrieval (CE205)

  • Digital and Technology Solutions End-Point Project (CE612)

  • Introduction to Programming in Python (CE705)

Publications

Journal articles (11)

Cordeiro de Amorim, R., Makarenkov, V. and Mirkin, B., Core clustering as a tool for tackling noise in cluster labels. Journal of Classification

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

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

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

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

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

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

Amorim, RC., (2013). Constrained Clustering with Minkowski Weighted K-Means

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

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

de Amorim, RC., (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

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

r.amorim@essex.ac.uk
+44 (0) 1206 872895

Location:

4B.522, Colchester Campus

Academic support hours:

Tuesdays: 15:00 to 16:00 Thursdays: 14:00 to 15:00

More about me