获取从 Python 中的树状图形成的集群列表

Question

我有一个单词列表，我对其执行了 TF-IDF 算法以获得前 100 个单词的列表。之后我应该执行聚类。现在我能够完成这两项任务（我正在共享代码和输入文件的相关部分，输出屏幕截图）。

我的查询是我想要在输出树状图中形成的集群列表，我该怎么做？Dendrogram 函数返回一个ax包含一些坐标和节点列表的元组。我如何操作它们以获取完整的集群列表。

以下是从输入文件中提取的内容。

"recommended stories dylan scott stat advertisement kate sheridan dylan scott dylan scott",
"email touting former representative mike fergusons genuine connection",
"email touting former representative mike ferguson \u2019",
"facebook donald trump fda hhs privacy policy",
"president trump appoints dr scott gottlieb",
"trade groups including novartis ag",
"bush alumni coalition supporting trump",
"online presidential transition analysis center",
"tennessee republican representative marsha blackburn",
"nonprofit global health care company",
"paula stannard ,\u201d said ladd wiley",
"bremberg returned calls seeking comment",
"0 \u2026. 0 \u2026 1c",
"2016 w ashington \u2014 let",
"take place ,\u201d said dr",
"\u201c selling baby parts .\u201d",
"health care companies whose boards",
"transition ,\u201d said lisa tofil",

以下是我正在使用的代码

punctuations = '''!()-[]{};:'\<>./?@#$%^&*_~'''
n_a =fin_a= ""
for file in os.listdir():
    if (file.endswith(".kwp")):
        with open(file) as f:
            #print(f.read())
            a = f.read()
            a = re.sub(r"\\[a-z0-9A-Z]+","",a)
            a = re.sub(r"\"","",a)
            a = re.sub(r"\,","",a)
            #a = re.sub("\\","",a)
            #print(a)
            for ch in a:
                if (ch not in punctuations):
                    n_a = n_a + ch
            n_a = n_a.lower()
            #print(n_a)
            #new_f = open("n")

        fin_a = fin_a + n_a 

tfidf_vectorizer = TfidfVectorizer(max_df=1,stop_words='english',use_idf=True)
tfd_mat = tfidf_vectorizer.fit_transform([n_a])
dense = tfd_mat.todense()
#print(len(dense[0].tolist()[0]))
ep = dense[0].tolist()[0]
phrase_scores = [pair for pair in zip(range(0, len(ep)), ep) if pair[1] > 0]
#print(phrase_scores)
#print(len(phrase_scores))
phrase_scores=sorted(phrase_scores, key=lambda t: t[1] * -1)[:100]
#rint(tfd_mat)
fin_term = []
terms = tfidf_vectorizer.get_feature_names()
with open("/home/laitkor/Desktop/New_Paul/kwp_top100.txt","w") as fl:
    for t in range(0,100):
        #print(t)
        key,valu = phrase_scores[t]
        #print(key)
        #print(valu)
        fl.write(terms[key]+'\n')
        fin_term.append(terms[key])
#print(fin_term)    
#print(phrase_scores[1:100])
dist = 1 - cosine_similarity(phrase_scores[1:100])
#print(dist)
linkage_matrix = ward(dist)
#print(linkage_matrix)
fig, ax = plt.subplots(figsize=(30, 30)) # set size
ax = dendrogram(linkage_matrix, orientation="right", labels=fin_term);
#print(ax)
#print(leaves)
plt.tick_params(\
    axis= 'x',          # changes apply to the x-axis
    which='both',      # both major and minor ticks are affected
    bottom='off',      # ticks along the bottom edge are off
    top='off',         # ticks along the top edge are off
    labelbottom='off')

plt.tight_layout() #show plot with tight layout

#uncomment below to save figure
plt.savefig('kw[enter image description here][1]p.png', dpi=200)

以下链接包含形成的树状图的输出

https://www.screencast.com/t/2MEc3ohBe

Answer 1

你需要事先知道你想要多少个集群；那么你可以使用：

from scipy.cluster.hierarchy import fcluster
fl = fcluster(cl,numclust,criterion='maxclust')

哪里cl是您的链接方法的输出，numclust是您想要获得的集群数量。