Advances in Data Analysis and Classification
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Functional data clustering: a survey
Advances in Data Analysis and Classification - Tập 8 - Trang 231-255 - 2013
Clustering techniques for functional data are reviewed. Four groups of clustering algorithms for functional data are proposed. The first group consists of methods working directly on the evaluation points of the curves. The second groups is defined by filtering methods which first approximate the curves into a finite basis of functions and second perform clustering using the basis expansion coeffi...... hiện toàn bộ
Robust clustering of functional directional data
Advances in Data Analysis and Classification - Tập 16 - Trang 181-199 - 2021
A robust approach for clustering functional directional data is proposed. The proposal adapts “impartial trimming” techniques to this particular framework. Impartial trimming uses the dataset itself to tell us which appears to be the most outlying curves. A feasible algorithm is proposed for its practical implementation justified by some theoretical properties. A “warping” approach is also introdu...... hiện toàn bộ
A Kendall correlation coefficient between functional data
Advances in Data Analysis and Classification - Tập 13 - Trang 1083-1103 - 2019
Measuring dependence is a very important tool to analyze pairs of functional data. The coefficients currently available to quantify association between two sets of curves show a non robust behavior under the presence of outliers. We propose a new robust numerical measure of association for bivariate functional data. We extend in this paper Kendall coefficient for finite dimensional observations to...... hiện toàn bộ
A latent class analysis of the public attitude towards the euro adoption in Poland
Advances in Data Analysis and Classification - Tập 8 - Trang 427-442 - 2013
Latent class analysis can be viewed as a special case of model–based clustering for multivariate discrete data. It is assumed that each observation comes from one of a number of classes, groups or subpopulations, with its own probability distribution. The overall population thus follows a finite mixture model. When observed, data take the form of categorical responses—as, for example, in public op...... hiện toàn bộ
Functional data clustering by projection into latent generalized hyperbolic subspaces
Advances in Data Analysis and Classification - Tập 15 Số 3 - Trang 735-757 - 2021
Eigenvalues and constraints in mixture modeling: geometric and computational issues
Advances in Data Analysis and Classification - Tập 12 - Trang 203-233 - 2017
This paper presents a review about the usage of eigenvalues restrictions for constrained parameter estimation in mixtures of elliptical distributions according to the likelihood approach. The restrictions serve a twofold purpose: to avoid convergence to degenerate solutions and to reduce the onset of non interesting (spurious) local maximizers, related to complex likelihood surfaces. The paper sho...... hiện toàn bộ
A Riemannian geometric framework for manifold learning of non-Euclidean data
Advances in Data Analysis and Classification - Tập 15 - Trang 673-699 - 2020
A growing number of problems in data analysis and classification involve data that are non-Euclidean. For such problems, a naive application of vector space analysis algorithms will produce results that depend on the choice of local coordinates used to parametrize the data. At the same time, many data analysis and classification problems eventually reduce to an optimization, in which the criteri...... hiện toàn bộ
From here to infinity: sparse finite versus Dirichlet process mixtures in model-based clustering
Advances in Data Analysis and Classification - Tập 13 - Trang 33-64 - 2018
In model-based clustering mixture models are used to group data points into clusters. A useful concept introduced for Gaussian mixtures by Malsiner Walli et al. (Stat Comput 26:303–324, 2016) are sparse finite mixtures, where the prior distribution on the weight distribution of a mixture with K components is chosen in such a way that a priori the number of clusters in the data is random and is all...... hiện toàn bộ
Tổng số: 418
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