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Attribute Exploration Using Implications with Proper Premises

机译:使用具有适当场所的含义的属性探索

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We propose a variation of the attribute exploration algorithm. Instead of implications with pseudo-intents as premises our approach uses implications with proper premises. It is known that the set of implications with proper premises is complete, but in general it is not minimal in size. This variation will allow us to calculate all implications of a formal context with premise size at most n, for some fixed n∈N. This is of interest if the attribute set is large and the user requests valid implications with small premises. Other applications can be seen for formal contexts where the maximal premise size of an implication with proper premise is known, for example multivalued contexts scaled by multiordinal scales only.
机译:我们提出了属性探索算法的变化。而不是与伪意图的影响,因为我们的方法使用具有适当房屋的含义。众所周知,具有适当房屋的含义集成,但通常它的尺寸不足。这种变化将允许我们在最多n个固定的n∈n,以最多n个尺寸计算正式上下文的所有含义。如果属性集很大,并且用户请求使用小型房屋的有效影响,则这是感兴趣的。可以看到其他应用程序对于正式的背景,已知具有适当前提的含义的最大前提大小,例如仅由多元尺度缩放的多值上下文。

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