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We propose a simple information-theoretic clustering approach based on maximizing the mutual information I(\sfx,y) between the unknown cluster labels y and the training patterns \sfx with respect to parameters of specifically constrained encoding dis ...
We propose to apply statistical clustering algorithms on a three-dimensional profile of red blood cells (RBCs) obtained through digital holographic microscopy (DHM). We show that two classes of RBCs stored for 14 and 38 days can be effectively classified. ...
We introduce a fast approach to classification and clustering applicable to high-dimensional continuous data, based on Bayesian mixture models for which explicit computations are available. This permits us to treat classification and clustering in a single ...
This work addresses the problem of reducing the time between query submission and results output in a retrieval system. The goal is achieved by considering only a database fraction as small as possible during the retrieval process. Our approach is based on ...
Many clustering methods are designed for especial cluster types or have good performance dealing with particular size and shape of clusters. The main problem in this connection is how to define a similarity (or dissimilarity) criterion to make an algorithm ...
One of the shortcomings of the existing clustering methods is their problems dealing with different shape and size clusters. On the other hand, most of these methods are designed for especial cluster types or have good performance dealing with particular s ...
The thesis is a contribution to extreme-value statistics, more precisely to the estimation of clustering characteristics of extreme values. One summary measure of the tendency to form groups is the inverse average cluster size. In extreme-value context, th ...
We propose a new technique for the identification of discrete-time hybrid systems in the Piece-Wise Affine (PWA) form. The identification algorithm proposed in (Ferrari-Trecate et. al., 2000) is first considered and then improved under various aspects. Mea ...
Mixture models form the essential basis of data clustering within a statistical framework. Here, the estimation of the parameters of a mixture of Gaussian densities is considered. In this particular context, it is well known that the maximum likelihood app ...
The R package bclust is useful for clustering high-dimensional continuous data. The package uses a parametric spike-and-slab Bayesian model to downweight the effect of noise variables and to quantify the importance of each variable in agglomerative cluster ...
