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的kMeans来对文本文档进行聚类。该示例代码可以正常运行，但需要约20个新闻组数据作为输入。我想使用相同的代码对文档列表进行聚类，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"]

要使用此列表作为输入，我需要在kMeans示例代码中进行哪些更改？（仅获取“数据集=文档”无效）

Answer 1

这是一个简单的示例：

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)

集群文件

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

打印每个群集的主要术语

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