11.2scikit-learn里的k-均值算法

-*- 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]

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

9个中心 print clf.cluster_centers_

每个样本所属的簇 print clf.labels_

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_

进行预测 print clf.predict(feature)

保存模型 joblib.dump(clf , 'c:/km.pkl')

载入保存的模型 clf = joblib.load('c:/km.pkl')

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