  Conventional rule learning algorithms aim at finding a set of simple rules,
where each rule covers as many examples as possible. In this paper, we argue
that the rules found in this way may not be the optimal explanations for each
of the examples they cover. Instead, we propose an efficient algorithm that
aims at finding the best rule covering each training example in a greedy
optimization consisting of one specialization and one generalization loop.
These locally optimal rules are collected and then filtered for a final rule
set, which is much larger than the sets learned by conventional rule learning
algorithms. A new example is classified by selecting the best among the rules
that cover this example. In our experiments on small to very large datasets,
the approach's average classification accuracy is higher than that of
state-of-the-art rule learning algorithms. Moreover, the algorithm is highly
efficient and can inherently be processed in parallel without affecting the
learned rule set and so the classification accuracy. We thus believe that it
closes an important gap for large-scale classification rule induction.
