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Association rule mining, one of the most important and well researched techniques of data mining, was first introduced in. It aims to extract interesting correlations, frequent patterns, associations or casual structures among sets of items in the transaction databases or other data repositories. However, no method has been shown to be able to handle data streams, as no method is scalable enough to manage the high rate which stream data arrive at. More recently, they have received attention from the data mining community and methods have been defined to automatically extract and maintain gradual rules from numerical databases. In this paper, the authors thus propose an original approach to mine data streams for Association rule mining.
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