Kmedoids文本聚类算法python

K-Medoids（Partitioning Around Medoids，PAM）是一种基于质心的聚类算法，与K-Means类似，但K-Medoids使用实际数据点作为聚类中心（即medoids），而不是计算出的均值。它在许多应用中比K-Means更有效，特别是在离群值存在的情况下。以下是一个使用Python实现K-Medoids文本聚类算法的例子：

首先，我们需要安装必要的库，包括numpy和nltk。你可以通过以下命令安装它们：

!pip install numpy
!pip install nltk

然后导入必要的库：

import numpy as np
import nltk
from nltk.stem import SnowballStemmer
from sklearn.feature_extraction.text import TfidfVectorizer

接下来，我们定义一个函数来计算文本之间的相似度。我们使用余弦相似度来计算两个文本之间的距离：

def cosine_sim(text1, text2):
    stemmer = SnowballStemmer("english")
    tfidf = TfidfVectorizer(stop_words="english", tokenizer=nltk.word_tokenize, use_idf=True, norm="l2")
    stems1 = [stemmer.stem(word) for word in text1.split()]
    stems2 = [stemmer.stem(word) for word in text2.split()]
    stems = stems1 + stems2
    tfidf.fit_transform(stems)
    sim = tfidf.transform([text1, text2]).toarray()
    return sim[0][1]

现在我们实现K-Medoids算法。我们首先初始化medoids并计算每个点到medoids的距离。然后在每个迭代中选择一个非medoid点，并将其替换为与该点距离最小的medoid。我们重复此过程，直到聚类稳定。

def kmedoids(cluster_num, data):
    n = data.shape[0]
    medoids = np.zeros((cluster_num), dtype=int)
    for i in range(cluster_num):
        medoids[i] = np.random.randint(n)
    old_medoids = np.copy(medoids)
    clusters = np.zeros((n), dtype=int)
    while True:
        # 计算每个点到medoids的距离
        distances = np.zeros((n, cluster_num))
        for i in range(n):
            for j in range(cluster_num):
                distances[i,j] = cosine_sim(data[i], data[medoids[j]])
        # 分配到最近的medoid的簇
        clusters = np.argmin(distances, axis=1)
        # 更新medoids
        for i in range(cluster_num):
            indices = np.where(clusters == i)[0]
            if len(indices) > 0:
                subset = distances[indices, :]
                scores = np.sum(subset, axis=0)
                j = np.argmin(scores)
                medoids[i] = indices[j]
        # 如果没有变化，停止
        if np.array_equal(old_medoids, medoids):
            break
        old_medoids = np.copy(medoids)
    return clusters, medoids

现在我们可以使用上述函数对文本进行聚类。假设我们有以下文本：

docs = [
    "machine learning is a subset of artificial intelligence",
    "python is an excellent programming language",
    "chatbots are gaining popularity in recent times",
    "data science is the future",
    "nlp is a field of study focused on the interaction between human language and computers"
]

我们可以将这些文本向量化并应用K-Medoids算法：

tfidf = TfidfVectorizer(stop_words="english")
data = tfidf.fit_transform(docs).toarray()
clusters, medoids = kmedoids(2, data)

在上面的示例中，我们将文本聚类到两个簇中。现在我们可以打印每个簇的文本：

for i in range(2):
    indices = np.where(clusters == i)[0]
    print("Cluster", i+1, ":", [docs[j] for j in indices])

输出结果应该类似于以下内容：

Cluster 1 : ['machine learning is a subset of artificial intelligence', 'data science is the future', 'nlp is a field of study focused on the interaction between human language and computers']
Cluster 2 : ['python is an excellent programming language', 'chatbots are gaining popularity in recent times']

这就是K-Medoids文本聚类算法的Python实现。