2015
DOI: 10.3390/e17041775
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Multidimensional Scaling Visualization Using Parametric Similarity Indices

Abstract: In this paper, we apply multidimensional scaling (MDS) and parametric similarity indices (PSI) in the analysis of complex systems (CS). Each CS is viewed as a dynamical system, exhibiting an output time-series to be interpreted as a manifestation of its behavior. We start by adopting a sliding window to sample the original data into several consecutive time periods. Second, we define a given PSI for tracking pieces of data. We then compare the windows for different values of the parameter, and we generate the … Show more

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Cited by 41 publications
(12 citation statements)
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“…In this Section, we compare the loci obtained with different nonlinearities by means of Procrustes analysis [69] , [70] , [71] , [72] . The Procrustes analysis takes a collection of loci and transforms them for obtaining the "best" superposition.…”
Section: Procrustes Analysis and Visualization Of Nonlinear Fractionamentioning
confidence: 99%
“…In this Section, we compare the loci obtained with different nonlinearities by means of Procrustes analysis [69] , [70] , [71] , [72] . The Procrustes analysis takes a collection of loci and transforms them for obtaining the "best" superposition.…”
Section: Procrustes Analysis and Visualization Of Nonlinear Fractionamentioning
confidence: 99%
“…The MDS is a technique for dimensionality reduction, clustering and computational visualization of multidimensional data [ 63 , 64 , 65 , 66 , 67 , 68 , 69 ]. Given L objects , , in a r -dim space and a measure of dissimilarity between the i th and j th objects, , the procedure starts by calculating a symmetric matrix, of object-to-object dissimilarities.…”
Section: Mathematical Backgroundmentioning
confidence: 99%
“…Clustering and visualizing data with a large number of attributes is overly important in science [ 50 , 51 , 52 , 53 , 54 ]. The MDS is a computational technique for dimensionality-reduction, clustering, and visualization of multidimensional data [ 33 , 55 , 56 , 57 ]. Given a set of objects , , in a r -dimensional space, and a measure of dissimilarity between the pair i and j , , the procedure starts by calculating an symmetric matrix, , of object-to-object dissimilarities.…”
Section: Mathematical Backgroundmentioning
confidence: 99%