document: Mining progressive confident rules Many real world objects have states that change over time . By tracking the state sequences of these objects , we can study their behavior and take preventive measures before they reach some undesirable states . In this paper , we propose a new kind of pattern called progressive confident rules to describe sequences of states with an increasing confidence that lead to a particular end state . We give a formal definition of progressive confident rules and their concise set . We devise pruning strategies to reduce the enormous search space . Experiment result shows that the proposed algorithm is efficient and scalable . We also demonstrate the application of progressive confident rules in classification .
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