11.2scikit-learn里的k-均值算法

1. -*- coding: utf-8 -*-

from sklearn.cluster import KMeans from sklearn.externals import joblib import numpy

final = open('c:/test/final.dat' , 'r')

data = [line.strip().split('\t') for line in final] feature = [[float(x) for x in row[3:]] for row in data]

1. 调用kmeans类

clf = KMeans(n_clusters=9) s = clf.fit(feature) print s

1. 9个中心

print clf.cluster_centers_

1. 每个样本所属的簇

print clf.labels_

1. 用来评估簇的个数是否合适，距离越小说明簇分的越好，选取临界点的簇个数

print clf.inertia_

1. 进行预测

print clf.predict(feature)

1. 保存模型

joblib.dump(clf , 'c:/km.pkl')

1. 载入保存的模型

clf = joblib.load('c:/km.pkl')

1. 用来评估簇的个数是否合适，距离越小说明簇分的越好，选取临界点的簇个数

for i in range(5,30,1):

   clf = KMeans(n_clusters=i)
   s = clf.fit(feature)
   print i , clf.inertia_

来源：网络