Data mining and machine learning algorithms usually operate directly on the data. However, if the data is not available at once or consists of billions of instances, these algorithms easily become infeasible with respect to memory and run-time concerns. As a solution to this problem, we propose a framework, called MiDEO (Mining Density Estimates inferred Online), in which algorithms are designed to operate on a condensed representation of the data. In particular, we propose to use density estimates, which are able to represent billions of instances in a compact form and can be updated when new instances arrive. As an example for an algorithm that operates on density estimates, we consider the task of mining association rules, which we consider as a form of simple statements about the data. The algorithm, called POEt (Pattern mining on Online density esTimates), is evaluated on synthetic and real-world data and is compared to state-of-the-art algorithms.
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