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This paper presents a partitional dynamic clustering method for interval data based on adaptive Hausdorff distances. Dynamic clustering algorithms are iterative two-step relocation algorithms involving the construction of the clusters at each iteration and the identification of a suitable representation or prototype (means, axes, probability laws, groups of… (More)
In order to extend the dynamical clustering algorithm to interval data sets, we define the prototype of a cluster by optimization of a classical adequacy criterion based on Hausdorff distance. Once this class prototype properly defined we give a simple and converging algorithm for this new type of interval data.
We propose in this paper an exploratory analysis algorithm for functional data. The method partitions a set of functions into K clusters and represents each cluster by a simple prototype (e.g., piecewise constant). The total number of segments in the prototypes, P , is chosen by the user and optimally distributed among the clusters via two dynamic… (More)
Estimation of fastest paths on large networks forms a crucial part of dynamic route guidance systems. The present paper proposes a statistical approach for predicting fastest paths on urban networks. The traffic data used for conducting the statistical analysis are the input flows, the arc states or the number of cars in the arcs and the different paths of… (More)
This paper introduces hard clustering algorithms that are able to partition objects taking into account simultaneously their relational descriptions given by multiple dissimilarity matrices. These matrices have been generated using different sets of variables and dissimilarity functions. These methods are designed to furnish a partition and a prototype for… (More)
Many data analysis methods cannot be applied to data that are not represented by a fixed number of real values, whereas most of real world observations are not readily available in such a format. Vector based data analysis methods have therefore to be adapted in order to be used with non standard complex data. A flexible and general solution for this… (More)
We propose in this paper an exploratory analysis algorithm for functional data. The method partitions a set of functions into K clusters and represents each cluster by a piecewise constant prototype. The total number of segments in the prototypes, P , is chosen by the user and optimally distributed into the clusters via two dynamic programming algorithms.
Résumé. Nous proposons dans cet article une méthode de visualisation de l’activité des utilisateurs d’un site web qui permet d’évaluer qualitativement l’adéquation entre son architecture logique et la perception de celle-ci par les internautes. Nous travaillons sur les parcours des internautes sur le site étudié, après reconstruction de ceux-ci grâce aux… (More)