An optimization method for reasoning results is presented, such as recursive grey fitting model for single sequence and attribute correlativity model for multi-dimension data.
分别在单序列时建立递进灰拟合模型，在多维数据集时利用属性相关性，对插值结果进行学习优化。
They perform frequency, mean, minimum, maximum, and standard deviation analysis on attribute data and save the results in a new table.
这些工具完成对属性数据的频次、平均值、最小值、最大值和标准偏差的分析，并将结果存于一个新的表格中。
The experimental results indicate that the algorithm is very successful in picking different types of attacks in data set based on different attribute features.
实验表明该算法针对不同特征属性的数据集，检测不同类型的入侵行为，具有很好的检测结果。
Experimental results show the algorithm is effective. It can find different reductions of attribute in the information system and provide more information for decision support and data mining.
实验证明该算法是有效的，并能求解出信息系统中多组不同的最小约简，为决策支持和数据挖掘等提供更多信息。
Experimental results show the algorithm is effective. It can find different reductions of attribute in the information system and provide more information for decision support and data mining.
实验证明该算法是有效的，并能求解出信息系统中多组不同的最小约简，为决策支持和数据挖掘等提供更多信息。
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