Mining Progressive Confident Rules
| dc.contributor.author | ZHANG, Minghua | en_US |
| dc.contributor.author | HSU, Wynne | en_US |
| dc.contributor.author | LEE, Mong Li | en_US |
| dc.date.accessioned | 2006-06-14T07:38:36Z | en_US |
| dc.date.accessioned | 2017-01-23T07:00:00Z | |
| dc.date.available | 2006-06-14T07:38:36Z | en_US |
| dc.date.available | 2017-01-23T07:00:00Z | |
| dc.date.issued | 2006-06-09 | en_US |
| dc.description.abstract | Many real world objects have states that change overtime. 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 propose new pruning strategies and employ the concise set analysis of rules in the mining process to reduce the enormous search space. Experiment result shows that the proposed algorithmis efficient and scalable. We also demonstrate the application of progressive confident rules in classification. | en_US |
| dc.format.extent | 537491 bytes | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.uri | https://dl.comp.nus.edu.sg/xmlui/handle/1900.100/2214 | en_US |
| dc.language.iso | en | en_US |
| dc.relation.ispartofseries | TRA6/06 | en_US |
| dc.title | Mining Progressive Confident Rules | en_US |
| dc.type | Technical Report | en_US |