document: Mining complex models from arbitrarily large databases in constant time In this paper we propose a scaling-up method that is applicable to essentially any induction algorithm based on discrete search . The result of applying the method to an algorithm is that its running time becomes independent of the size of the database , while the decisions made are essentially identical to those that would be made given infinite data . The method works within pre-specified memory limits and , as long as the data is iid , only requires accessing it sequentially . It gives anytime results , and can be used to produce batch , stream , time-changing and active-learning versions of an algorithm . We apply the method to learning Bayesian networks , developing an algorithm that is faster than previous ones by orders of magnitude , while achieving essentially the same predictive performance . We observe these gains on a series of large databases `` generated from benchmark networks , on the KDD Cup 2000 e-commerce data , and on a Web log containing 100 million requests .
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