Lucas X. T. Bezerra

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The recording of symbolic interval data has become a common practice with the recent advances in database technologies. This paper introduces a dynamic clustering method to partitioning symbolic interval data. This method furnishes a partition and a prototype for each cluster by optimizing an adequacy criterion that measures the fitting between the clusters(More)
This paper introduce a new criterion and two new linear regression methods to predict interval-valued data. The proposed approaches consist in a new point of view to study the relationship between the midpoints and the ranges of the interval-valued variables. The evaluation of the proposed prediction methods is based on the average behaviour of the root(More)
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