KNN是通过测量不同特征值之间的距离进行分类。它的的思路是：如果一个样本在特征空间中的k个最相似(即特征空间中最邻近)的样本中的大多数属于某一个类别，则该样本也属于这个类别。K通常是不大于20的整数。KNN算法中，所选择的邻居都是已经正确分类的对象。该方法在定类决策上只依据最邻近的一个或者几个样本的类别来决定待分样本所属的类别。(NN is classified by measuring the distance between the different eigenvalues. It is the idea that the sample also belongs to this category if the majority of k samples of the sample in the feature space are the most similar (ie, the nearest neighbor in the feature space). K is usually an integer greater than 20. In the KNN algorithm, the selected neighbors are objects that have been correctly classified. The method determines the category of the sample to be sorted only on the classification decision based on the nearest one or several samples.)