Multidimensional scaling of fuzzy dissimilarity data

Fuzzy Dissimilarity based Multidimensional Scaling and its Application to Collaborative Learning Data

Usage of Dissimilarity Measures and Multidimensional Scaling for Large Scale Solar Data Analysis

This work describes the application of several dissimilarity measures combined with multidimensional scaling for large scale solar data analysis. Using the first solar domain-specific benchmark data set that contains multiple types of phenomena, we investigated combinations of different image parameters with different dissimilarity measures in order to determine which combinations will allow us...

Fuzzy multidimensional scaling

Multidimensional scaling (MDS) is a data analysis technique for representing measurements of (dis)similarity among pairs of objects as distances between points in a low-dimensional space. MDS methods differ mainly according to the distance model used to scale the proximities. The most usual model is the Euclidean one, although a spherical model is often preferred to represent correlation measur...

Multidimensional Scaling and Data Clustering

Visualizing and structuring pairwise dissimilarity data are difficult combinatorial optimization problems known as multidimensional scaling or pairwise data clustering. Algorithms for embedding dissimilarity data set in a Euclidian space, for clustering these data and for actively selecting data to support the clustering process are discussed in the maximum entropy framework. Active data select...

Multidimensional scaling of interval-valued dissimilarity data

Multidimensional scaling is a well-known technique for representing measurements of dissimilarity among objects as points in a p-dimensional space. In this paper, this method is extended to the case where dissimilarities are only known to lie within certain intervals. Each object is then no longer represented as point, but as a region of R p , i n s u c h a w ay that the minimum and maximum dis...