The known hierarchical clustering scheme is equivalent to the concept of ul- trametric distance. AII distance can be represented in a spatial model using multidimensional scaling. We relate both clases of representations of proxim- ity data in an algebraic way, obtaining Borne results and relations on clusters and the eigenvalues of the inner product matrix for an ultrametric distance. Principal coordinate analysis on an ultrametric distance gives two classes of independent coordinates, describing compact clusters and representing objects inside every cluster.Peer Reviewe
Part 5: Classification - ClusteringInternational audienceIn many cases of high dimensional data anal...
<p>Distance metrics are based on the Euclidean distance single linkage method (proximity matrix).</p
This paper explores hierarchical clustering in the case where pairs of points have dissimilarity sco...
The known hierarchical clustering scheme is equivalent to the concept of ultrametric distance. Every...
An ultrametric topology formalizes the notion of hierarchical structure. Anultrametric embedding, re...
The distribution of distances between points in a high-dimensional data set tends to look quite diff...
We study hierarchical clustering schemes under an axiomatic view. We show that within this framework...
Coding of data, usually upstream of data analysis, has crucial impli- cations for the data analysis ...
Manifold multidimensional concepts are explained via a tree-shape structure by taking into account t...
Many relevant multidimensional phenomena, such as well-being, climate change, sustainable developmen...
An ultrametric topology formalizes the notion of hierarchical structure. An ultrametric embedding, r...
Multidimensional scaling has a wide range of applications when observations are not continuous but i...
<p>Multidimensional scaling has a wide range of applications when observations are not continuous bu...
Abstract We formulate and (approximately) solve hierarchical versions of two prototypical problems i...
Part 5: Classification - ClusteringInternational audienceIn many cases of high dimensional data anal...
<p>Distance metrics are based on the Euclidean distance single linkage method (proximity matrix).</p
This paper explores hierarchical clustering in the case where pairs of points have dissimilarity sco...
The known hierarchical clustering scheme is equivalent to the concept of ultrametric distance. Every...
An ultrametric topology formalizes the notion of hierarchical structure. Anultrametric embedding, re...
The distribution of distances between points in a high-dimensional data set tends to look quite diff...
We study hierarchical clustering schemes under an axiomatic view. We show that within this framework...
Coding of data, usually upstream of data analysis, has crucial impli- cations for the data analysis ...
Manifold multidimensional concepts are explained via a tree-shape structure by taking into account t...
Many relevant multidimensional phenomena, such as well-being, climate change, sustainable developmen...
An ultrametric topology formalizes the notion of hierarchical structure. An ultrametric embedding, r...
Multidimensional scaling has a wide range of applications when observations are not continuous but i...
<p>Multidimensional scaling has a wide range of applications when observations are not continuous bu...
Abstract We formulate and (approximately) solve hierarchical versions of two prototypical problems i...
Part 5: Classification - ClusteringInternational audienceIn many cases of high dimensional data anal...
<p>Distance metrics are based on the Euclidean distance single linkage method (proximity matrix).</p
This paper explores hierarchical clustering in the case where pairs of points have dissimilarity sco...