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首页> 外文期刊>International Journal of Uncertainty, Fuzziness, and Knowledge-based Systems >S-mountain Method For Obtaining Focus Points From Data
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S-mountain Method For Obtaining Focus Points From Data

机译：从数据中获取焦点的S-mountain方法

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摘要

We introduce a variation of the Mountain Method called the S-Mountain Method, an algorithm for finding focus points from data points based on the similarity of a focus point with the data points. An important aspect of this algorithm is the association of energy levels with the data points and the reduction of these energy levels as we discover focus points. The rate of reduction of the energy level at a data point is related to its similarity with the recently discovered focus point. We provide a general form for this reduction operation using the t-norm operators. We show how this S-Mountain algorithm can be used to discover focus elements that are granular objects such as fuzzy subsets. Finally we extend the S-Mountain method to environments where our similarities are drawn from an ordinal scale. Here we discuss the interesting question of comparing objects consisting of collections of ordinal values.
机译：我们介绍了一种称为S-Mountain方法的Mountain方法的变体，该算法是根据焦点与数据点的相似性从数据点中查找焦点的算法。该算法的一个重要方面是能量水平与数据点的关联以及在我们发现焦点时这些能量水平的降低。数据点能量水平的降低速率与其与最近发现的焦点的相似性有关。我们使用t-norm运算符为该约简操作提供了一种通用形式。我们展示了如何使用这种S-Mountain算法来发现焦点元素，这些焦点元素是诸如模糊子集之类的颗粒状对象。最后，我们将S-Mountain方法扩展到从有序尺度得出相似性的环境。在这里，我们讨论一个有趣的问题，即比较由序数值集合组成的对象。

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