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Fuzzy Orderings for Fuzzy Gradual Patterns

机译:模糊渐变模式的模糊排序

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In mining gradual patterns the idea is to express co-variations of attributes, taking the direction of change of attribute values into account. These patterns are such as {the more A, the more B}, {the more A, the more B, the less C} or { the higher the speed, the higher the danger }. These patterns are denoted as {A≥B≥ }, { A≥B≥C≤} or { speed≥danger≥} respectively. Such patterns hold if the variation constraints simultaneously hold on the attributes. However, it is often hardly possible to compare attribute values, either because the values are taken from noisy data, or because it is difficult to consider that a small difference between two values is meaningful. In this context, we focus on the use of fuzzy orderings to take this into account, abstract environment.
机译:在挖掘渐变模式时,其思想是表达属性的协变,同时考虑属性值的变化方向。这些模式如{越多,B越多},{越多,B越多,C越少}或{速度越高,危险越高}。这些模式分别表示为{A≥B≥},{A≥B≥C≤}或{速度≥危险≥}。如果变异约束同时适用于属性,则此类模式适用。但是,由于属性值是从嘈杂的数据中获取的,或者因为很难认为两个值之间的微小差异是有意义的,因此通常很难比较属性值。在这种情况下,我们专注于使用模糊排序将抽象环境考虑在内。

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