Mining negative generalized knowledge from relational databases

Yu Ying Wu, Yen Liang Chen, Ray I. Chang

研究成果: 雜誌貢獻期刊論文同行評審

9 引文 斯高帕斯(Scopus)

摘要

Attribute-oriented induction (AOI) is a useful data mining method for extracting generalized knowledge from relational data and users' background knowledge. Concept hierarchies can be integrated with the AOI method to induce multi-level generalized knowledge. However, the existing AOI approaches are only capable of mining positive knowledge from databases; thus, rare but important negative generalized knowledge that is unknown, unexpected, or contradictory to what the user believes, can be missed. In this study, we propose a global negative attribute-oriented induction (GNAOI) approach that can generate comprehensive and multiple-level negative generalized knowledge at the same time. Two pruning properties, the downward level closure property and the upward superset closure property, are employed to improve the efficiency of the algorithm, and a new interest measure, nim(cl), is exploited to measure the degree of the negative relation. Experiment results from a real-life dataset show that the proposed method is effective in finding global negative generalized knowledge.

原文???core.languages.en_GB???
頁(從 - 到)134-145
頁數12
期刊Knowledge-Based Systems
24
發行號1
DOIs
出版狀態已出版 - 2月 2011

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