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中文摘要：
在知识发现过程中用户感兴趣的往往是一些高层次、适当概括的简化信息，面向属性的归纳是目前主要的数据归约方法，一般是仅考虑原始数据所提供简单的统计信息．本文提出的基于量化扩展概念格的属性归纳算法，采用概念的爬升进行相应的泛化来完成多层、多属性归纳．与面向属性归纳算法比较，该算法的泛化路径不是唯一的，在量化扩展概念格的哈斯图中容易找到合适的泛化路径和阈值，得到满足用户要求合理的属性归纳结果，以提供用户所需的不同粒度的知识．
英文摘要：
In knowledge discovery in databases （KDD）, users show much interest in high-level, general and reductive information. Attribute oriented induction （AOI）, which generally takes the statistical information from original data into account, has been commonly used in data reduction. However, attribute-oriented algorithm based on quantitative concept lattice can finish induction with multi-level and multi-attribute by using concept ascension according to the Hasse diagram of the quantitative extended concept lattice. Compared with AOI, the generalization path of the proposed algorithm is not unique. The proper generalization paths and thresholds on the Hasse diagram of quantitative extended concept lattice could be found easily. The required reasonable results are gotten, and different granular knowledge is provided for users.
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