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            <title xml:lang="en">MultiSOM: a multiview neural model for accurately analyzing and mining complex data</title>
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                <forename type="first">Jean-Charles</forename>
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                    <forename type="first">Jean-Charles</forename>
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                <term xml:lang="en">multiple viewpoints</term>
                <term xml:lang="en">data analysis</term>
                <term xml:lang="en">unsupervised learning</term>
                <term xml:lang="en">SOM model</term>
                <term xml:lang="en">MultiSOM model</term>
                <term xml:lang="en">symbolic model</term>
                <term xml:lang="en">knowledge extraction</term>
                <term xml:lang="en">association rules</term>
                <term xml:lang="en">patent analysis</term>
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              <p>In the procedure of information analysis, like in the domain of patent analysis, the complexity of the studied topics and the accuracy of the question to be answered may often lead the analyst to partition his reasoning into viewpoints. Most of the classical information analysis tools can only manage an analysis of the studied domain in a global way. The information analysis tool is considered in this paper is the MultiSOM tool, whose core model represents a significant extension of the classical Kohonen SOM neural model. The MultiSOM neural-based tool introduces the concepts of viewpoints and dynamics into the information analysis with its multi-maps displays and its inter-map communication process. The dynamic information exchange between maps can be exploited by an analyst in order to perform cooperative deduction between several different analyzes that have been performed on the same data. This paper demonstrates the efficiency of a viewpoint-oriented analysis as compared to a global analysis in the domain of patents. Both objective and subjective quality criteria are taken into account for quality evaluation.</p>
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