python 聚类分析实战案例:K-means算法(原理源码)

K-means算法：

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，

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 lists   indices = 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 = newPartition      newMeans = [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 random   k = 3   partition = 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()