Renata M. C. R. de Souza

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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)
Unsupervised pattern recognition methods for mixed feature-type symbolic data based on dynamical clustering methodology with adaptive distances are presented. These distances change at each algorithm’s iteration and can either be the same for all clusters or different from one cluster to another. Moreover, the methods need a previous pre-processing step in(More)
This paper introduces different pattern classifiers for interval data based on the logistic regression methodology. Four approaches are considered. These approaches differ according to the way of representing the intervals. The first classifier considers that each interval is represented by the centres of the intervals and performs a classic logistic(More)