Abstract
The implementation of data mining methods on dataflow computers enables an easy use of parallelism, but it also faces numerous obstacles. The problem underlying the impossibility of using currently developed algorithms in their existing form is their adaptation to von Neumann computer model, which assumes sequential calculations and intensive use of memory. This is one of the reasons why there are no fully developed classification algorithms on dataflow computer models in the open literature at the moment when this text is written. This chapter summarizes the characteristics that can be used as directions in the future construction of algorithms and outlines drafts for two implementations of the K-nearest neighbor algorithm.
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