首页> 外文会议>Second International Symposium on Electronic Commerce and Security(第二届电子商务与安全国际研究大会)(ISECS 2009)论文集 >Fuzzy Comprehensive Evaluation on Digital Libraries Based on Membership Degree Transformation New Algorithm-M(1,2,3)
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Fuzzy Comprehensive Evaluation on Digital Libraries Based on Membership Degree Transformation New Algorithm-M(1,2,3)

机译:基于隶属度变换新算法-M(1,2,3)的数字图书馆模糊综合评价

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摘要

In the evaluation process of digital libraries, there are many aspects needing to consider, with a lot of uncertainty and ambiguity, so it is reasonable and scientific to apply fuzzy comprehensive evaluation method for digital library fuzzy evaluation. The core of fuzzy evaluation is membership degree transformation. But the existing transformation methods should be questioned, because redundant data in index membership degree is also used to compute object membership degree, which is not useful for object classification. The new algorithm is: using data mining technology based on entropy to mine knowledge information about object classification hidden in every index, affirm the relationship of object classification and index membership, eliminate the redundant data in index membership for object classification by defining distinguishable weight and extract valid values to compute object membership. The new algorithm of membership degree transformation includes three calculation steps which can be summarized as "effective, comparison and composition", which is denoted as M(1,2,3)- The paper applied the new algorithm in the fuzzy evaluation of digital libraries.
机译:在数字图书馆评估过程中,有很多方面需要考虑,不确定性和模糊性很大,因此将模糊综合评估法应用于数字图书馆的模糊评估是合理而科学的。模糊评价的核心是隶属度转换。但是现有的转换方法值得质疑,因为索引成员资格度中的冗余数据也被用于计算对象成员资格度,这对对象分类没有用处。新算法是:利用基于熵的数据挖掘技术,挖掘隐藏在每个索引中的对象分类知识信息,确认对象分类与索引成员的关系,通过定义可区分的权重,消除对象分类的索引成员冗余数据,提取出计算对象成员资格的有效值。隶属度变换的新算法包括三个计算步骤,可以概括为“有效,比较,组成”,分别表示为M(1,2,3)。 。

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