# 最大最小距离算法的Python实现
# 数据集形式data=[[],[],...,[]]
# 聚类结果形式result=[[[],[],...],[[],[],...],...]
# 其中[]为一个模式样本，[[],[],...]为一个聚类import mathdef start_cluster(data, t):zs = [data[0]]  # 聚类中心集，选取第一个模式样本作为第一个聚类中心Z1# 第2步：寻找Z2,并计算阈值TT = step2(data, t, zs)# 第3,4,5步，寻找所有的聚类中心get_clusters(data, zs, T)# 按最近邻分类result = classify(data, zs, T)return result# 分类
def classify(data, zs, T):result = [[] for i in range(len(zs))]for aData in data:min_distance = Tindex = 0for i in range(len(zs)):temp_distance = get_distance(aData, zs[i])if temp_distance < min_distance:min_distance = temp_distanceindex = iresult[index].append(aData)return result# 寻找所有的聚类中心
def get_clusters(data, zs, T):max_min_distance = 0index = 0for i in range(len(data)):min_distance = []for j in range(len(zs)):distance = get_distance(data[i], zs[j])min_distance.append(distance)min_dis = min(dis for dis in min_distance)if min_dis > max_min_distance:max_min_distance = min_disindex = iif max_min_distance > T:zs.append(data[index])# 迭代get_clusters(data, zs, T)# 寻找Z2,并计算阈值T
def step2(data, t, zs):distance = 0index = 0for i in range(len(data)):temp_distance = get_distance(data[i], zs[0])if temp_distance > distance:distance = temp_distanceindex = i# 将Z2加入到聚类中心集中zs.append(data[index])# 计算阈值TT = t * distancereturn T# 计算两个模式样本之间的欧式距离
def get_distance(data1, data2):distance = 0for i in range(len(data1)):distance += pow((data1[i]-data2[i]), 2)return math.sqrt(distance)if __name__=='__main__':data = [[0, 0], [3, 8], [1, 1], [2, 2], [5, 3], [4, 8], [6, 3], [5, 4], [6, 4], [7, 5]]t = 0.5  #比例因子result = start_cluster(data, t)for i in range(len(result)):print("----------第" + str(i+1) + "个聚类----------")print(result[i])

----------第1个聚类----------
[[0, 0], [1, 1], [2, 2]]
----------第2个聚类----------
[[3, 8], [4, 8]]
----------第3个聚类----------
[[5, 3], [6, 3], [5, 4], [6, 4], [7, 5]]