Minghua Zhang
ABSTRACT
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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Digital Library
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- M. Zhang and W. Hsu and M. L. Lee, Mining Progressive Confident Rules, Dept. of Computer Science, National University of Singapore, 2006, June, TRA6/06Google Scholar
Index Terms
- Mining progressive confident rules
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