聚类是一种无监督学习算法。kmeans是通过发现给定数据集的k个簇的算法。簇个数k是用户给定的，每一个簇通过其质心。及簇中所有点的中心来描述。
请打开链接下载测试 testSet.txt 文件: https://github.com/caichunbing/kmeans/blob/master/testSet.zip
#================================================================
# Copyright (C) 2019 * Ltd. All rights reserved.
#
# Editor : pycharm
# File name : kmeans.py
# Author : caichunbing
# Created date: 2019-10-18
# Description :kmeans聚类算法及可视化
#
#================================================================
import numpy as np
import matplotlib.pyplot as plt
def loadDataSet(fileName): # general function to parse tab -delimited floats
dataMat = [] # assume last column is target value
fr = open(fileName)
for line in fr.readlines():
curLine = line.strip().split('\t')
curLine=[float(x) for x in curLine]
# fltLine = map(float, curLine) # map all elements to float()
dataMat.append(curLine)
return np.mat(dataMat)
def distEclud(vecA, vecB):
dist=np.sqrt(np.sum(np.power(vecA - vecB, 2)))
return dist # la.norm(vecA-vecB)
def randCent(dataSet, k):
n = np.shape(dataSet)[1]
centroids = np.mat(np.zeros((k, n))) # create centroid mat
for j in range(n): # create random cluster centers, within bounds of each dimension
minJ = min(dataSet[:, j])
rangeJ = float(max(dataSet[:, j]) - minJ)
centroids[:, j] = np.mat(minJ + rangeJ * np.random.rand(k, 1))
return centroids
def kMeans(dataSet, k, distMeas=distEclud, createCent=randCent):
m = np.shape(dataSet)[0]
clusterAssment = np.mat(np.zeros((m, 2))) # create mat to assign data points
# to a centroid, also holds SE of each point
centroids = createCent(dataSet, k)
clusterChanged = True
while clusterChanged:
clusterChanged = False
for i in range(m): # for each data point assign it to the closest centroid
minDist = np.inf
minIndex = -1
for j in range(k):
distJI = distMeas(centroids[j, :], dataSet[i, :])
if distJI < minDist:
minDist = distJI
minIndex = j
if clusterAssment[i, 0] != minIndex: clusterChanged = True
clusterAssment[i, :] = minIndex, minDist ** 2
print(centroids)
for cent in range(k): # recalculate centroids
ptsInClust = dataSet[np.nonzero(clusterAssment[:, 0].A == cent)[0]] # get all the point in this cluster
centroids[cent, :] = np.mean(ptsInClust, axis=0) # assign centroid to mean
return centroids, clusterAssment
def show(w,h,centroid_w,centroid_h):
fig = plt.figure()
fig.suptitle("kmeans")
ax1 = fig.add_subplot(1, 1, 1)
ax1.scatter(w, h, s=10, color='b')
ax1.scatter(centroid_w,centroid_h,s=10,color='r')
plt.show()
filename="./testSet.txt"
if __name__ == '__main__':
dataSet=loadDataSet(filename)
centoid,cluster=kMeans(dataSet, 4,distEclud,randCent)
w = dataSet[:, 0].tolist()
h = dataSet[:, 1].tolist()
centoid_w=centoid[:,0].tolist()
centoid_h=centoid[:,1].tolist()
show(w,h,centoid_w,centoid_h)
效果图：
