首先声明本文章部分内容借鉴于OpenCV实现最大最小距离聚类算法_pan_jinquan的博客-CSDN博客_opencv 聚类算法
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一、算法基本思想
最大最小距离法是模式识别中一种基于试探的类聚算法，它以欧式距离为基础，取尽可能远的对象作为聚类中心。因此可以避免K-means法初值选取时可能出现的聚类种子过于临近的情况，它不仅能智能确定初试聚类种子的个数，而且提高了划分初试数据集的效率。
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二、算法实现步骤
测试是在二维平面上选取十个样本点，坐标分别为：{x1(0 0), x2(3 8), x3(2 2), x4(1 1), x5(5 3), x6(4 8), x7(6 3), x8(5 4), x9(6 4), x10(7 5)}，其样本分布如图所示（图1）：
Original: https://blog.csdn.net/thequitesunshine007/article/details/121925486
Author: thequitesunshine007
Title: 基于OpenCV实现的最大最小距离聚类算法实战案例
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