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Practical construction of k-nearest neighbor graphs in metric spaces
(SPRINGER-VERLAG BERLIN, 2006)
Let U be a set of elements and d a distance function defined among them. Let NNk (u) be the k elements in U - {u} having the smallest distance to u. The k-nearest neighbor graph (kNNG) is a weighted directed graph G(U,E) ...
Regular graph construction for semi-supervised learning
(IOP PublishingBristol, 2014)
Semi-supervised learning (SSL) stands out for using a small amount of labeled points for data clustering and classification. In this scenario graph-based methods allow the analysis of local and global characteristics of ...
New Graph Based Trust Similarity Measure
(Universidade Federal de Santa Maria, 2015)
A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks
(Elsevier B.V., 2015-02-10)
We propose a nature-inspired approach to estimate the probability density function (pdf) used for data clustering based on the optimum-path forest algorithm (OPFC). OPFC interprets a dataset as a graph, whose nodes are the ...
Network-based data classification: combining k-associated optimal graphs and high-level prediction
(SpringerDordrecht, 2014-06-17)
Background: Traditional data classification techniques usually divide the data space into sub-spaces, each representing a class. Such a division is carried out considering only physical attributes of the training data ...
Clustering gene expression data with a penalized graph-based metric
(BioMed Central, 2012)
Clustering gene expression data with a penalized graph-based metric
(Biomed Central, 2011-01)
Background: The search for cluster structure in microarray datasets is a base problem for the so-called “-omic sciences”. A difficult problem in clustering is how to handle data with a manifold structure, i.e. data that ...