document: Mining time-changing data streams Most statistical and machine-learning algorithms assume that the data is a random sample drawn from a stationary distribution . Unfortunately , most of the large databases available for mining today violate this assumption . They were gathered over months or years , and the underlying processes generating them changed during this time , sometimes radically . Although a number of algorithms have been proposed for learning time-changing concepts , they generally do not scale well to very large databases . In this paper we propose an efficient algorithm for mining decision trees from continuously-changing data streams , based on the ultra-fast VFDT decision tree learner . This algorithm , called CVFDT , stays current while making the most of old data by growing an alternative subtree whenever an old one becomes questionable , and replacing the old with the new when the new becomes more accurate . CVFDT learns a model which is similar in accuracy to the one that would be learned by reapplying VFDT to a moving window of examples every time a new example arrives , but with O ( 1 ) complexity per example , as opposed to O ( w ) , where w is the size of the window . Experiments on a set of large time-changing data streams demonstrate the utility of this approach .
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