CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A hierarchical mixture model for clustering three-way data sets
Computational Statistics & Data Analysis
Dynamic clustering of interval data using a Wasserstein-based distance
Pattern Recognition Letters
Dimensionality reduction when data are density functions
Computational Statistics & Data Analysis
A dimensionally reduced finite mixture model for multilevel data
Journal of Multivariate Analysis
Far beyond the classical data models: symbolic data analysis
Statistical Analysis and Data Mining
New approaches to nonparametric density estimation and selection of smoothing parameters
Computational Statistics & Data Analysis
Model-based clustering of high-dimensional data: A review
Computational Statistics & Data Analysis
Editorial: The 2nd special issue on advances in mixture models
Computational Statistics & Data Analysis
Dynamic clustering of histogram data based on adaptive squared Wasserstein distances
Expert Systems with Applications: An International Journal
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The problem of clustering probability density functions is emerging in different scientific domains. The methods proposed for clustering probability density functions are mainly focused on univariate settings and are based on heuristic clustering solutions. New aspects of the problem associated with the multivariate setting and a model-based perspective are investigated. The novel approach relies on a hierarchical mixture modeling of the data. The method is introduced in the univariate context and then extended to multivariate densities by means of a factorial model performing dimension reduction. Model fitting is carried out using an EM-algorithm. The proposed method is illustrated through simulated experiments and applied to two real data sets in order to compare its performance with alternative clustering strategies.