Francisco de A. T. de Carvalho

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This paper presents adaptive and non-adaptive fuzzy c-means clustering methods for partitioning symbolic interval data. The proposed methods furnish a fuzzy partition and prototype for each cluster by optimizing an adequacy criterion based on suitable squared Euclidean distances between vectors of intervals. Moreover, various cluster interpretation tools(More)
This paper introduces a new approach to fitting a linear regression model to symbolic interval data. Each example of the learning set is described by a feature vector, for which each feature value is an interval. The new method fits a linear regression model on the mid-points and ranges of the interval values assumed by the variables in the learning set.(More)
Different clustering techniques such as Self-Organizing Map (SOM), and hierarchical clustering, among others, have been applied to gene expression data. The focuses of theses studies are often on the biological results, and there is no indication on what methods are more suitable for clustering gene expression. In this paper, an evaluation methodology that(More)
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 the computational analysis of gene expression time series, the main aspect in finding co-expressed genes is the proximity (similarity or dissimilarity) index used in the clustering method. In this context, the proximity indices should find genes that have similar patterns of expression change through time. There are a number of proximity indices used for(More)