K-means算法：
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关于步骤：参考之前的博客
关于代码与数据：暂时整理代码如下：后期会附上github地址，上传原始数据与代码完整版，
各种聚类算法的对比：参考连接
Kmeans算法的缺陷
1.聚类中心的个数K 需要事先给定，但在实际中这个 K 值的选定是非常难以估计的，很多时候，事先并不知道给定的数据集应该分成多少个类别才最合适
2.Kmeans需要人为地确定初始聚类中心，不同的初始聚类中心可能导致完全不同的聚类结果。
#!usr/bin/env python#_*_ coding:utf-8 _*_import randomimport math'''kMeans:2列数据对比，带有head'''#1.load datadef importData():f = lambda name,b,d: [name, float(b), float(d)]with open('birth-death-rates.csv', 'r') as inputFile:return [f(*line.strip().split('\t')) for line in inputFile]
写入文件类型
**#2. calculate Distance **
def euclideanDistance(x,y):return math.sqrt(sum([(a-b)**2 for (a,b) in zip(x,y)]))#L=points,def partition(points, k, means, d=euclideanDistance):# print('means={}'.format(means))thePartition = [[] for _ in means] # list of k empty listsindices = range(k)# print('indices={}'.format(indices))for x in points:#index为indices索引，调用d函数，计算每个值与聚类中心的距离，将其分类closestIndex = min(indices, key=lambda index: d(x, means[index]))#实现X与每个Y直接的求解：key=lambda index: d(x, means[index])thePartition[closestIndex].append(x)return thePartition
#3.寻找收敛点def mean(points):''' assume the entries of the list of points are tuples;e.g. (3,4) or (6,3,1). '''n = len(points)# print(tuple(float(sum(x)) / n for x in zip(*points))) #*points将【[1，2]，[2，3]】分割出来【1，2】return tuple(float(sum(x)) / n for x in zip(*points)) #将最开始的[[4, 1], [1, 5]] 经过处理变成[（4, 1）,（1, 5）]def kMeans(points, k, initialMeans, d=euclideanDistance):oldPartition = []newPartition = partition(points, k, initialMeans, d)while oldPartition != newPartition:oldPartition = newPartitionnewMeans = [mean(S) for S in oldPartition]newPartition = partition(points, k, newMeans, d)return newPartition
#0.函数调用初始中心点
if __name__ == "__main__":L = [x[1:] for x in importData()] # remove names# print (str(L).replace('[','{').replace(']', '}'))import matplotlib.pyplot as plt'''plt.scatter(*zip(*L))plt.show()'''import randomk = 3partition = kMeans(L, k, random.sample(L, k)) #L是集合，K分类个数，random.sample(L, k)中心点plt.scatter(*zip(*partition[0]), c='b')#[[],[],[]]plt.scatter(*zip(*partition[1]), c='r')plt.scatter(*zip(*partition[2]), c='g')plt.show()
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