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Browsing by Author "Cordeiro De Amorim, Renato"
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A-Wardpβ : Effective hierarchical clustering using the Minkowski metric and a fast k-means initialisation
Cordeiro De Amorim, Renato; Makarenkov, Vladimir; Mirkin, Boris (2016-11-20)In this paper we make two novel contributions to hierarchical clustering. First, we introduce an anomalous pattern initialisation method for hierarchical clustering algorithms, called A-Ward, capable of substantially ... -
Anomalous pattern based clustering of mental tasks with subject independent learning : some preliminary results
Cordeiro De Amorim, Renato; Mirkin, Boris; Q. Gan, John (2012-09-01)In this paper we describe a new method for EEG signal classification in which the classification of one subject’s EEG signals is based on features learnt from another subject. This method applies to the power spectrum ... -
Applying subclustering and Lp distance in Weighted K-Means with distributed centroids
Cordeiro De Amorim, Renato; Makarenkov, Vladimir (2016-01-15)We consider the Weighted K-Means algorithm with distributed centroids aimed at clustering data sets with numerical, categorical and mixed types of data. Our approach allows given features (i.e., variables) to have different ... -
Between Sound and Spelling : Combining Phonetics and Clustering Algorithms to Improve Target Word Recovery
Zampieri, Marcos; Cordeiro De Amorim, Renato (Springer Nature, 2014)In this paper we revisit the task of spell checking focusing on target word recovery. We propose a new approach that relies on phonetic information to improve the accuracy of clustering algorithms in identifying misspellings ... -
Challenges in developing Capture-HPC exclusion lists
Puttaroo, Mohammad; Komisarczuk, Peter; Cordeiro De Amorim, Renato (ACM Press, 2014-09-09)In this paper we discuss the challenges faced whilst developing exclusion lists for the high-interaction client honeypot, Capture-HPC. Exclusion lists are Capture client system behaviours which are used in the decision ... -
A clustering based approach to reduce feature redundancy
Cordeiro De Amorim, Renato; Mirkin, Boris (Springer Nature, 2016-02-26)Research effort has recently focused on designing feature weighting clustering algorithms. These algorithms automatically calculate the weight of each feature, representing their degree of relevance, in a data set. However, ... -
Constrained Clustering with Minkowski Weighted K-Means
Cordeiro De Amorim, Renato (Institute of Electrical and Electronics Engineers (IEEE), 2012)In this paper we introduce the Constrained Minkowski Weighted K-Means. This algorithm calculates cluster specific feature weights that can be interpreted as feature rescaling factors thanks to the use of the Minkowski ... -
Effective Spell Checking Methods Using Clustering Algorithms
Cordeiro De Amorim, Renato; Zampieri, Marcos (Association for Computational Linguistics, 2013)This paper presents a novel approach to spell checking using dictionary clustering. The main goal is to reduce the number of times distances have to be calculated when finding target words for misspellings. The method is ... -
An Empirical Evaluation of Different Initializations on the Number of K-Means Iterations
Cordeiro De Amorim, Renato (Springer Nature, 2013)This paper presents an analysis of the number of iterations K-Means takes to converge under different initializations. We have experimented with seven initialization algorithms in a total of 37 real and synthetic datasets. ... -
Feature Relevance in Ward’s Hierarchical Clustering Using the Lp Norm
Cordeiro De Amorim, Renato (2015-04)In this paper we introduce a new hierarchical clustering algorithm called Ward p . Unlike the original Ward, Ward p generates feature weights, which can be seen as feature rescaling factors thanks to the use of the L p ... -
Feature weighting as a tool for unsupervised feature selection
Panday, Deepak; Cordeiro De Amorim, Renato; Lane, Peter (2018-01-01)Feature selection is a popular data pre-processing step. The aim is to remove some of the features in a data set with minimum information loss, leading to a number of benefits including faster running time and easier data ... -
Feature Weighting for Clustering : Using K-Means and the Minkowski Metric
Cordeiro De Amorim, Renato (LAP Lambert Academic publishing, 2012) -
A method for classifying mental tasks in the space of EEG transforms
Cordeiro De Amorim, Renato; Mirkin, Boris; Q. Gan, John (2009)In this article we describe a new method for supervised classification of EEG signals. This method applies to the power spectrum density data and assigns class-dependent information weights to individual pixels, so that ... -
The Minkowski central partition as a pointer to a suitable distance exponent and consensus partitioning
Cordeiro De Amorim, Renato; Shestkov, Andrei; Mirkin, Boris; Makarenkov, Vladimir (2017-07-31)The Minkowski weighted K-means (MWK-means) is a recently developed clustering algorithm capable of computing feature weights. The cluster-specific weights in MWK-means follow the intuitive idea that a feature with low ... -
Minkowski Metric for Feature Weighting
Cordeiro De Amorim, Renato; Mirkin, Boris (2011-07) -
Minkowski Metric, Feature Weighting and Anomalous Cluster Initialisation in K-Means Clustering
Cordeiro De Amorim, Renato; Mirkin, Boris (2012-03)This paper represents another step in overcoming a drawback of K-Means, its lack of defense against noisy features, using feature weights in the criterion. The Weighted K-Means method by Huang et al. (2008, 2004, 2005) ... -
On Drive-by-Download Attacks and Malware Classification
Puttaaroo, Mohammad; Komisarczuk, Peter; Cordeiro De Amorim, Renato (North East Wales Institute, 2013-09) -
On Initializations for the Minkowski Weighted K-Means
Cordeiro De Amorim, Renato; Komisarczuk, Peter (Springer Nature, 2012)Minkowski Weighted K-Means is a variant of K-Means set in the Minkowski space, automatically computing weights for features at each cluster. As a variant of K-Means, its accuracy heavily depends on the initial centroids ... -
On Partitional Clustering of Malware
Cordeiro De Amorim, Renato; Komisarczuk, Peter (2012-07)In this paper we fully describe a novel clustering method for malware, from the transformation of data into a manipulable standardised data matrix, finding the number of clusters until the clustering itself including ... -
Partitional Clustering of Malware Using K-Means
Cordeiro De Amorim, Renato; Komisarczuk, Peter (Springer Nature, 2014-05)This paper describes a novel method aiming to cluster datasets containing malware behavioural data. Our method transform the data into an standardised data matrix that can be used in any clustering algorithm, finds the ...