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 for clustering text documents. The example code 工作正常，但需要大约 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"]

我需要在 kMeans example code 中做哪些更改才能将此列表用作输入？ （单纯取'dataset = documents'不行）

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"]

矢量化文本，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)

打印每个集群集群的热门术语

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

如果您想更直观地了解它的外观，请参阅 。