在 Python 中使用 scikit-learn kmeans 对文本文档进行聚类

Question

我需要实现scikit-learn's kMeans 来聚类文本文档。 example code 工作正常，但需要一些 20newsgroups 数据作为输入。我想使用相同的代码来聚类文档列表，如下所示：

documents = ["Human machine interface for lab abc computer applications",
             "A survey of user opinion of computer system response time",
             "The EPS user interface management system",
             "System and human system engineering testing of EPS",
             "Relation of user perceived response time to error measurement",
             "The generation of random binary unordered trees",
             "The intersection graph of paths in trees",
             "Graph minors IV Widths of trees and well quasi ordering",
             "Graph minors A survey"]

我需要在kMeans example code 中进行哪些更改才能将此列表用作输入？ （简单地采用“数据集 = 文档”是行不通的）

Answer 1

这是一个更简单的例子：

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
from sklearn.metrics import adjusted_rand_score

documents = ["Human machine interface for lab abc computer applications",
             "A survey of user opinion of computer system response time",
             "The EPS user interface management system",
             "System and human system engineering testing of EPS",
             "Relation of user perceived response time to error measurement",
             "The generation of random binary unordered trees",
             "The intersection graph of paths in trees",
             "Graph minors IV Widths of trees and well quasi ordering",
             "Graph minors A survey"]

向量化文本，即将字符串转换为数字特征

vectorizer = TfidfVectorizer(stop_words='english')
X = vectorizer.fit_transform(documents)

集群文档

true_k = 2
model = KMeans(n_clusters=true_k, init='k-means++', max_iter=100, n_init=1)
model.fit(X)

打印每个集群集群的热门词

print("Top terms per cluster:")
order_centroids = model.cluster_centers_.argsort()[:, ::-1]
terms = vectorizer.get_feature_names()
for i in range(true_k):
    print "Cluster %d:" % i,
    for ind in order_centroids[i, :10]:
        print ' %s' % terms[ind],
    print

如果您想更直观地了解其外观，请参阅this answer。