Roberto Avogadri

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OBJECTIVE Two major problems related the unsupervised analysis of gene expression data are represented by the accuracy and reliability of the discovered clusters, and by the biological fact that the boundaries between classes of patients or classes of functionally related genes are sometimes not clearly defined. The main goal of this work consists in the(More)
Two major problems related the unsupervised analysis of gene expression data are represented by the accuracy and reliability of the discovered clusters, and by the biological fact that classes of examples or classes of functionally related genes are sometimes not clearly defined. To face these items, we propose a fuzzy ensemble clustering approach to both(More)
The validation of clusters discovered in bio-molecular data is a central issue in bioinformatics. Recently, stability-based methods have been successfully applied to the analysis of the reliability of clusterings characterized by a relatively low number of examples and clusters. Nevertheless , several problems in functional genomics are characterized by a(More)
In data mining, clustering is one of the major tasks and aims at grouping the data objects into meaningful classes (clusters) such that the similarity of objects within clusters is maximized, and the similarity of objects between clusters is minimized. The dataset sometimes may be in mixed nature that is it may consist of both numeric and categorical type(More)
A stability-based algorithm to validate hierarchical clusters of genes 319 Abstract: Stability-based methods have been successfully applied in functional genomics to the analysis of the reliability of clusterings characterised by a relatively low number of examples and clusters. The application of these methods to the validation of gene clusters discovered(More)
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