A new density-based sampling for clustering algorithm - Ros, Frederic;Guillaume, Serge
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As clustering algorithms become more and more sophisticated to cope with current needs, large data sets of increasing complexity, sampling is likely to provide an interesting alternative. The main objective of sampling is to select a part that behaves like the whole. DENDIS is a new algorithm that combines the best of the available techniques in such a way that tractability is actually improved with a user friendly parameter setting.

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Produktbeschreibung
As clustering algorithms become more and more sophisticated to cope with current needs, large data sets of increasing complexity, sampling is likely to provide an interesting alternative. The main objective of sampling is to select a part that behaves like the whole. DENDIS is a new algorithm that combines the best of the available techniques in such a way that tractability is actually improved with a user friendly parameter setting.
Autorenporträt
Frederic Ros began his career in 1991 as a research scientist working on the field of image analysis. He managed the vision activity in GEMALTO during 14 years which is the world leader in the smart card industry. He has been researcher at PRISME laboratory (Orleans) for 10 years and head of an innovation park for 8 years.