摘要
通过分析现有Vague集相似度量方法,指出这些方法虽然满足相似度量基本准则,但是在描述Vague集相似性上存在不足之处,并且度量的准确度较低,在分析Vague值的支持度、反对度及其补集之间的最大最小值关系的基础上研究Vague集相似度量方法,给出一种新的Vague集相似度量方法,并证明该方法满足相似度量的基本准则。通过与现有相似度量方法的比较,说明该相似度量方法克服了现有相似度量方法的不足,符合人们的直观感受,能够合理、有效地区分数据。
By analysing existing similarity measure methods for Vague set, we point out that though they meet basic rules of similarity measure, but the defects exist in describing the similarity of Vague set~ and the measurement accuracy is also low. We analyse the support and opposing degrees of the Vague value and the relationship of maximal and minimal values between its complement set, on this basis we study the similarity measure of Vague set, and propose a new similarity measure method for Vague set. Furthermore, we prove that the new method satisfies the basic rules of similarity measure. By comparing it with existing similarity measure methods, it is illustrated that this method overcomes the defects of current similarity measure methods, accords with the intuitive sense of people, and can reasonably and effectively distinguish the data.
出处
《计算机应用与软件》
CSCD
北大核心
2013年第9期259-262,共4页
Computer Applications and Software
基金
宁夏大学科学研究基金项目(ZR1146)
关键词
VAGUE集
相似度量
最大值
最小值
Vague set Similarity measure Maximal value Minimal value
作者简介
赵雪芬，讲师，主研领域：统计学与人工智能的数学基础。
