From data to global generalized knowledge |
Abstract:
The attribute-oriented induction (AOI) is a useful data mining method that extracts generalized knowledgefrom relational data and user's background knowledge. The method uses two thresholds, the relation thresholdand attribute threshold, to guide the generalization process, and output generalized knowledge, a set ofgeneralized tuples which describes the major characteristics of the target relation. Although AOI has beenwidely used in various applications, a potential weakness of this method is that it only provides a snapshotof the generalized knowledge, not a global picture. When thresholds are different, we would obtain differentsets of generalized tuples, which also describe the major characteristics of the target relation. If a user wantsto ascertain a global picture of induction, he or she must try different thresholds repeatedly. That is time-consumingand tedious. In this study, we propose a global AOI (GAOI) method, which employs the multiple-levelmining technique with multiple minimum supports to generate all interesting generalized knowledge at onetime. Experiment results on real-life dataset show that the proposed method is effective in finding globalgeneralized knowledge. |
| Keywords: |
Attribute-oriented induction Data mining Multiple-level mining Generalized knowledge |
| Author(s): |
Yen-Liang Chen , Yu-Ying Wu b, Ray-I Chang |
| Source: |
Decision Support Systems 52 (2012) 295–307 |
| Subject: |
مدیریت دانش |
| Category: |
مقاله مجله |
| Release Date: |
2012 |
| No of Pages: |
13 |
| Price(Tomans): |
0 |
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