1.学会了Python的文本聚类：大致步骤是分词、获取词频矩阵和词袋、进行聚类
import codecs import pandas as pd data=pd.read_csv("scien1.csv") col=data.iloc[:,1] arrs=col.values import jieba stopwords = {}.fromkeys(['，', '。', '！', '这', '我', '非常']) # 精确模式 print(u" 中文分此后结果：") curpus=[] for a in arrs: seglist=jieba.cut(a,cut_all=False) final = '' for seg in seglist: if seg not in stopwords: final=final+seg seg_list=jieba.cut(final,cut_all=False) output=' '.join(list(seg_list)) curpus.append(output) #%% from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfTransformer #将文本中的词语转换为词频矩阵 vectorizer=CountVectorizer(curpus) #计算每个词语出现的次数 transformer = TfidfTransformer() tfidf = transformer.fit_transform(vectorizer.fit_transform(curpus)) x=vectorizer.fit_transform(curpus) #获取词袋中所有文本关键词 word=vectorizer.get_feature_names() print('') docs_matrix = pd.np.array(x.toarray()) print(docs_matrix) weight = tfidf.toarray() resName = "BaiduTfidf_Result.txt" result = codecs.open(resName, 'w', 'utf-8') for j in range(len(word)): result.write(word[j] + ' ') print(word[j] + ' ') result.write(' ') #%% for i in range(len(weight)): for j in range(len(word)): result.write(str(weight[i][j]) + ' ') result.write(' ') #%% from sklearn.cluster import KMeans clf = KMeans(n_clusters=20) s = clf.fit(weight) print(s) print(clf.cluster_centers_) print(clf.labels_) i = 1 number=[] while i <= len(clf.labels_): print (i, clf.labels_[i-1]) number.append(clf.labels_[i-1]) i = i + 1 print(number) #用来评估簇的个数是否合适，距离越小说明簇分的越好，选取临界点的簇个数 #print(clf.inertia_) #%% lable = [] #存储408个类标 20个类 content = [] first=pd.read_csv("scien1.csv") first['julei']=number #%% first.head() asdf=first.to_csv("result.csv")
2.GL关系图的使用
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