1 - 8 of 8 articles
We describe a new wavelet transform, for use on hierarchies or binary rooted trees. The theoretical framework of this approach to data analysis is described. Case studies are used to further exemplify this approach. A first set of application studies deals with data array smoothing, or...
When a dissimilarity matrix cannot be represented in a Euclidean space, it is possible to make it Euclidean by means of suitable transformations of the original dissimilarity values. In this paper we discuss some interesting properties of a class of transformations based on adding a specific...
Feedforward neural networks are a popular tool for classification, offering a method for fully flexible modeling. This paper looks at the underlying probability model, so as to understand statistically what is going on in order to facilitate an intelligent choice of prior for a fully Bayesian...
In this paper two techniques for units clustering and factorial dimensionality reduction of variables and occasions of a three-mode data set are discussed. These techniques can be seen as the simultaneous version of two procedures based on the sequential application of k-means and Tucker2...
K-means clustering is arguably the most popular technique for partitioning data. Unfortunately, K-means suffers from the well-known problem of locally optimal solutions. Furthermore, the final partition is dependent upon the initial configuration, making the choice of starting partitions all the...
This paper presents asymmetric
agglomerative hierarchical clustering algorithms in an extensive view point. First, we develop a new updating formula for these algorithms, proposing a general framework to incorporate many algorithms. Next we propose measures to evaluate the fit of asymmetric...
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