Python进行KMeans聚类是比较简单的，首先需要import numpy，从sklearn.cluster中import KMeans模块：

import numpy as npfrom sklearn.cluster import KMeans

然后读取txt文件，获取相应的数据并转换成numpy array：

X = []f = open('rktj4.txt')for v in f:    regex = re.compile('\s+')    X.append([float(regex.split(v)[3]), float(regex.split(v)[6])])X = np.array(X)

设置类的数量，并聚类：

n_clusters = 5cls = KMeans(n_clusters).fit(X)

完整代码：

import numpy as npfrom sklearn.cluster import KMeansimport matplotlib.pyplot as pltimport reX = []f = open('rktj4.txt')for v in f:    regex = re.compile('\s+')    X.append([float(regex.split(v)[3]), float(regex.split(v)[6])])X = np.array(X)n_clusters = 5cls = KMeans(n_clusters).fit(X)cls.labels_markers = ['^','x','o','*','+']for i in range(n_clusters):    members = cls.labels_ == i    plt.scatter(X[members, 0], X[members, 1], s=60, marker=markers[i], c='b', alpha=0.5)    print     plt.title('')plt.show()

运行结果：