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An automatic clustering method based on distance evaluation function

机译:基于距离评估功能的自动聚类方法

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In spatial clustering, the key factor to solve the problem of optimal class number is to construct a proper cluster validity function. The value of k must be confirmed in advance to exert K-means algorithm. However, it can not be clearly and easily confirmed in fact for its uncertainty. This paper recommends a distance evaluation function based on Euclidean distance to confirm the optimal class number, designs a new optimization algorithm of k value. The experiential rule which is usually expressed as kmax n is theoretically proved to be reasonable. Results come from the example also show the validity of this new algorithm.
机译:在空间聚类中,解决最优分类数问题的关键是构建适当的聚类有效性函数。必须事先确认k的值才能使用K-means算法。但是,由于其不确定性,实际上并不能清晰,容易地得到确认。本文推荐了一种基于欧几里得距离的距离评估函数来确定最优类别数,设计了一种新的k值优化算法。理论上通常用kmax n表示的经验规则是合理的。实例的结果也表明了该新算法的有效性。

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