Rare class association rule mining with multiple imbalanced attributes
- Publication Type:
- Chapter
- Citation:
- Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection, 2009, pp. 66 - 75
- Issue Date:
- 2009-12-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
 | 2009001894OK.pdf | 4.28 MB |
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In this chapter, the authors propose a novel framework for rare class association rule mining. In each class association rule, the right-hand is a target class while the left-hand may contain one or more attributes. This algorithm is focused on the multiple imbalanced attributes on the left-hand. In the proposed framework, the rules with and without imbalanced attributes are processed in parallel. The rules without imbalanced attributes are mined through a standard algorithm while the rules with imbalanced attributes are mined based on newly defined measurements. Through simple transformation, these measurements can be in a uniform space so that only a few parameters need to be specified by user. In the case study, the proposed algorithm is applied in the social security field. Although some attributes are severely imbalanced, rules with a minority of imbalanced attributes have been mined efficiently. © 2010, IGI Global.
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