Semantic association is defined as the representation of rich knowledge about binary relation in semantic data model.
语义关联是语义数据模型中实体之间二维关系的知识表示形式。
It analyzes the express method of affair and association rule in the binary system sequences set and complexity in space and time.
分析了事务与关联规则在二进制序列集中的表示方法及其在空间、时间上的复杂度。
Firstly, the deficiency of two popular methods, measure of ordinal association and ROC analysis, for validation of binary regression models was illustrated by a designed numerical example.
首先通过一个数值例子，说明了顺序关联度量与ROC分析两种常用方法在二项响应回归模型预测能力验证方面存在的不足。
Through defining information granule with binary string, we introduce an algorithm of mining association rules based on granular computing.
通过采用二进制串表示所定义的信息粒，提出了基于粒计算的关联规则挖掘算法。
And making an experiment on it, it proves that binary system sequences set is efficient and feasible as an approach of organization data based on mining of association rules.
通过实验验证，在关联规则数据挖掘中采用二进制序列集这一组织数据方法是有效且可行的。
Aiming at the issues on the method of quantitative association rule mining based on section partition, this paper introduces a method based on decimal-binary conversion.
针对基于区间划分的数值型关联规则分析方法存在的问题，提出了一种基于进制转换的分析方法。
Aiming at the issues on the method of quantitative association rule mining based on section partition, this paper introduces a method based on decimal-binary conversion.
针对基于区间划分的数值型关联规则分析方法存在的问题，提出了一种基于进制转换的分析方法。
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