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|Title:||Mining progressive confident rules|
|Authors:||Zhang, M. |
|Citation:||Zhang, M.,Hsu, W.,Lee, M.L. (2006). Mining progressive confident rules. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2006 : 803-808. ScholarBank@NUS Repository.|
|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. Copyright 2006 ACM.|
|Source Title:||Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|
|Appears in Collections:||Staff Publications|
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