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New similarity index based on the aggregation of membership functions through OWA operator

机译:基于OWA运算符的隶属函数聚合的新相似度索引

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In the field of data analysis, the use of metrics is a classical way to assess pairwise similarity. Unfortunately the popular distances are often inoperative because of the noise, the multidimensionality and the heterogeneous nature of data. These drawbacks lead us to propose a similarity index based on fuzzy set theory. Each object of the dataset is described with the vector of its fuzzy attributes. Thanks to aggregation operators, the object is fuzzified by using the fuzzy attributes. Thus each object becomes a fuzzy subset within the dataset. The similarity of a reference object compared to another one is assessed through the membership function of the fuzzified reference object and an aggregation method using OWA operator.
机译:在数据分析领域,指标的使用是评估两两相似性的经典方法。不幸的是,由于噪声,多维性和数据的异质性,流行的距离通常无法使用。这些缺点使我们提出了一种基于模糊集理论的相似度指标。用其模糊属性的向量描述数据集的每个对象。多亏了聚合运算符,通过使用模糊属性来模糊化对象。因此,每个对象成为数据集中的模糊子集。通过模糊化参考对象的隶属函数和使用OWA运算符的聚合方法,可以评估参考对象与另一个对象的相似性。

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