Measures of Dissimilarities for Contrasting Information Sources in Data Fusion
Information content of data coming from a given source is modelled and formalized as a probability distribution and, as such, considered as a point in a function space, where a concept of distance can be introduced.
In such a space, Kullback-Leibler Information is a contrast function able to measure dissimilarities between probability distributions and, then, a practical index for clustering different information sources according to the quality of their content.
KeywordsCluster Criterion Contrast Function Leibler Information Reliability Data Source Reliability Data Collection
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