Renato Cordeiro de Amorim
Renato Cordeiro de Amorim
Senior Lecturer in Computer Science and AI at the University of Essex
Verified email at - Homepage
Cited by
Cited by
Minkowski metric, feature weighting and anomalous cluster initializing in K-Means clustering
RC de Amorim, B Mirkin
Pattern Recognition 45 (3), 1061–1075, 2012
Recovering the number of clusters in data sets with noise features using feature rescaling factors
RC De Amorim, C Hennig
Information sciences 324, 126-145, 2015
Feature relevance in Ward’s hierarchical clustering using the Lp norm
R Cordeiro de Amorim
Journal of Classification 32 (1), 46-62, 2015
A survey on feature weighting based K-Means algorithms
RC de Amorim
Journal of Classification 33 (2), 210-242, 2016
Feature weighting as a tool for unsupervised feature selection
D Panday, RC de Amorim, P Lane
Information processing letters 129, 44-52, 2018
Applying subclustering and L p distance in Weighted K-Means with distributed centroids
RC de Amorim, V Makarenkov
Neurocomputing 173 (3), 700--707, 2016
Effective Spell Checking Methods Using Clustering Algorithms
RC de Amorim, M Zampieri
Recent Advances in Natural Language Processing, 172-178, 2013
On Initializations for the Minkowski Weighted K-Means
RC de Amorim, P Komisarczuk
Lecture Notes in Computer Science, 45--55, 2012
Constrained Clustering with Minkowski Weighted K-Means
RC de Amorim
Proceedings of the 13th IEEE International Symposium on Computational …, 2012
Constrained Intelligent K-Means: Improving Results with Limited Previous Knowledge.
RC de Amorim
The Second International Conference on Advanced Engineering Computing and …, 2008
A-Wardpβ: Effective hierarchical clustering using the Minkowski metric and a fast k-means initialisation
RC de Amorim, V Makarenkov, B Mirkin
Information Sciences 370, 343-354, 2016
An Empirical Evaluation of Different Initializations on the Number of K-means Iterations
RC de Amorim
Lecture Notes in Computer Science 7629, 15-26, 2013
Unsupervised feature selection for large data sets
RC de Amorim
Pattern Recognition Letters 128, 183-189, 2019
The Minkowski central partition as a pointer to a suitable distance exponent and consensus partitioning
RC de Amorim, A Shestakov, B Mirkin, V Makarenkov
Pattern Recognition 67, 62-72, 2017
Between Sound and Spelling: Combining Phonetics and Clustering Algorithms to Improve Target Word Recovery
M Zampieri, RC de Amorim
Proceedings of the 9th International Conference on Natural Language Processing, 2014
On partitional clustering of malware
RC de Amorim, P Komisarczuk
The First International Workshop on Cyber Patterns: Unifying Design Patterns …, 2012
Weighting features for Partition Around Medoids using the Minkowski metric
RC de Amorim, T Fenner
Lecture Notes in Computer Science, 35--44, 2012
Learning feature weights for K-Means clustering using the Minkowski metric
RC de Amorim
Birkbeck, University of London, 2011
Computational Methods of Feature Selection, Huan Liu, Hiroshi Motoda, CRC Press, Boca Raton, FL (2007). 440 pp., Price: $93.95, ISBN: 978-1-58488-878-9
RC de Amorim
Information Processing & Management 45 (4), 490-493, 2009
Challenges in developing Capture-HPC exclusion lists
M Puttaroo, P Komisarczuk, RC de Amorim
Proceedings of the 7th International Conference on Security of Information …, 2014
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